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LOGIQ E10 is intended for use by a qualified physician for ultrasound evaluation of Fetal/Obstetrics; Abdominal (including Renal, Gynecology/Pelvic); Pediatric; Small Organ (Breast, Testes, Thyroid); Neonatal Cephalic, Adult Cephalic; Cardiac (Adult and Pediatric); Peripheral Vascular; Musculo-skeletal Conventional and Superficial; Urology (including Prostate); Transrectal; Transvaginal; Tranesophageal and Intraoperative (Abdominal and Vascular).

Modes of operation include: B, M, PW Doppler, CW Doppler, Color Doppler, Color M Doppler, Power Doppler, Harmonic Imaging, Coded Pulse, 3D/4D Imaging mode, Elastography, Shear Wave Elastography, Attenuation Imaging and combined modes: B/M, B/Color, B/PWD, B/Color/PWD, B/Power/PWD.

The LOGIQ E10 is intended to be used in a hospital or medical clinic.

The LOGIQ E10 is a full featured, Track 3, general purpose diagnostic ultrasound system which consists of a mobile console approximately 585 mm wide (keyboard), 991 mm deep and 1300 mm high that provides digital acquisition, processing and display capability. The user interface includes a computer keyboard, specialized controls, 12-inch high-resolution color touch screen and 23.8-inch High Contrast LED LCD monitor.

Here's an analysis of the acceptance criteria and supporting studies for the LOGIQ E10 ultrasound system, derived from the provided FDA 510(k) Clearance Letter:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size for Test Set and Data Provenance

• Auto Abdominal Color Assistant 2.0:
  • Test Set Sample Size: 49 individual subjects, 1186 annotation images.
  • Data Provenance: Retrospective, all data from the USA.
• Auto Aorta Measure Assistant:
  • Test Set Sample Size:
    • Long View Aorta: 36 subjects (11 Male, 25 Female).
    • Short View Aorta: 35 subjects (11 Male, 24 Female).
  • Data Provenance: Retrospective, from Japan (15-16 subjects) and USA (20 subjects).
• Auto Common Bile Duct (CBD) Measure Assistant:
  • Test Set Sample Size: 25 subjects (11 Male, 14 Female).
  • Data Provenance: Retrospective, from USA (40%) and Japan (60%).
• Ultrasound Guided Fat Fraction (UGFF):
  • Test Set Sample Size (Primary Study): 582 participants.
  • Data Provenance (Primary Study): Retrospective, Japan.
  • Test Set Sample Size (Confirmatory Study 1): 15 US patients + 5 EU patients (total 20).
  • Data Provenance (Confirmatory Study 1): Retrospective, USA and EU.
  • Test Set Sample Size (Confirmatory Study 2): 24 EU patients.
  • Data Provenance (Confirmatory Study 2): Retrospective, EU.

3. Number of Experts and Qualifications for Ground Truth

• Auto Abdominal Color Assistant 2.0: Not explicitly stated, but implies multiple "readers" to ground truth anatomical visibility. No specific qualifications are mentioned beyond "readers."
• Auto Aorta Measure Assistant: Not explicitly stated, but implies multiple "readers" for measurements and an "arbitrator" to select the most accurate measurement. No specific qualifications are mentioned beyond "readers" and "arbitrator."
• Auto Common Bile Duct (CBD) Measure Assistant: Not explicitly stated, but implies multiple "readers" for measurements and an "arbitrator" to select the most accurate measurement. No specific qualifications are mentioned beyond "readers" and "arbitrator."
• Ultrasound Guided Fat Fraction (UGFF): Ground truth for the primary study was MRI Proton Density Fat Fraction (MRI-PDFF %). No human experts were involved in establishing the ground truth for UGFF, as it relies on MRI-PDFF as the reference. The correlation between UGFF and UDFF also used UDFF as a reference, not human experts.

4. Adjudication Method for the Test Set

• Auto Abdominal Color Assistant 2.0: Not explicitly mentioned, however, the process described as "Readers to ground truth the 'anatomy' visible in static B-Mode image. (Before running AI)" and then comparing to AI predictions does not suggest an adjudication process for the ground truth generation itself beyond initial reader input. Confusion matrices were generated later.
• Auto Aorta Measure Assistant: An "Arbitrator" was used to "select most accurate measurement among all readers" for the initial ground truth, which was then compared to AI baseline. This implies a 1 (arbitrator) + N (readers) adjudication method for measurement accuracy. For keystroke comparison, readers measured with and without AI.
• Auto Common Bile Duct (CBD) Measure Assistant: An "Arbitrator" was used to "select most accurate measurement among all readers" for the initial ground truth, which was then compared to AI baseline. This implies a 1 (arbitrator) + N (readers) adjudication method for measurement accuracy. For keystroke comparison, readers measured with and without AI.
• Ultrasound Guided Fat Fraction (UGFF): Ground truth was established by MRI-PDFF or comparison to UDFF. No human adjudication method was described for these.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• Auto Aorta Measure Assistant: Yes, a comparative study was performed by comparing keystroke counts with and without AI assistance for human readers.
  • Effect Size:
    • Long View Aorta AP Measurement: Average reduction from $4.132 \pm 0.291$ keystrokes (without AI) to $1.236 \pm 0.340$ keystrokes (with AI).
    • Short View Aorta AP and Trans Measurement: Average reduction from $7.05 \pm 0.158$ keystrokes (without AI) to $2.307 \pm 1.0678$ keystrokes (with AI).
• Auto Common Bile Duct (CBD) Measure Assistant: Yes, a comparative study was performed by comparing keystroke counts with and without AI assistance for human readers.
  • Effect Size: Average reduction of $1.62 \pm 0.375$ keystrokes (mean and standard deviation) from manual to AI-assisted measurements.
• Other features (Auto Abdominal Color Assistant 2.0, UGFF): The documentation does not describe a MRMC study for improved human reader performance with AI assistance for these features.

6. Standalone (Algorithm Only) Performance Study

• Auto Abdominal Color Assistant 2.0: Yes, the model's accuracy (detection accuracy, sensitivity, specificity, DICE score) was evaluated in a standalone manner against the human-annotated ground truth.
• Ultrasound Guided Fat Fraction (UGFF): Yes, the correlation and agreement of the UGFF algorithm's values were tested directly against an established reference standard (MRI-PDFF) and another device's derived fat fraction (UDFF).

7. Type of Ground Truth Used

• Auto Abdominal Color Assistant 2.0: Expert consensus/annotations on B-Mode images, followed by comparison to AI predictions.
• Auto Aorta Measure Assistant: Expert consensus on measurements (human readers with arbitrator selection) and keystroke counts from these manual measurements and AI-assisted measurements.
• Auto Common Bile Duct (CBD) Measure Assistant: Expert consensus on measurements (human readers with arbitrator selection) and keystroke counts from these manual measurements and AI-assisted measurements.
• Ultrasound Guided Fat Fraction (UGFF): Established clinical reference standard: MRI Proton Density Fat Fraction (MRI-PDFF %). For one confirmatory study, another cleared device's derived fat fraction (UDFF) was used as a comparative reference.

8. Sample Size for the Training Set

• The document states that "The exams used for test/training validation purpose are separated from the ones used during training process" but does not provide the sample size for the training set itself for any of the AI features.

9. How the Ground Truth for the Training Set was Established

• The document implies that the ground truth for training data would have been established similarly to the test data ground truth (e.g., expert annotation for Auto Abdominal Color Assistant, expert measurements for Auto Aorta/CBD Measure Assistants). However, the specific methodology for the training set's ground truth establishment (e.g., number of experts, adjudication, qualifications) is not detailed in the provided text. It only explicitly states that "Before the process of data annotation, all information displayed on the device is removed and performed on information extracted purely from Ultrasound B-mode images" for annotation. Independence of test and training data by exam site origin or overall separation is mentioned, but not the process for creating the training set ground truth.

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The LOGIQ E10s is intended for use by a qualified physician for ultrasound evaluation.

Specific clinical applications and exam types include: Fetal / Obstetrics; Abdominal (including Renal, Gynecology / Pelvic); Pediatric; Small Organ (Breast, Testes, Thyroid); Neonatal Cephalic; Adult Cephalic; Cardiac (Adult and Pediatric); Peripheral Vascular; Musculo-skeletal Conventional and Superficial; Urology (including Prostate); Transrectal; Transvaginal; Transesophageal and Intraoperative (Abdominal and Vascular).

Modes of operation include: B, M, PW Doppler, CW Doppler, Color Doppler, Color M Doppler, Power Doppler, Harmonic Imaging, Coded Pulse, 3D/4D Imaging mode, Elastography, Shear Wave Elastography, Attenuation Imaging and Combined modes: B/M, B/Color, B/PWD, B/Color/PWD, B/Power/PWD.

The LOGIQ E10s is intended to be used in a hospital or medical clinic.

The LOGIQ E10s is a full featured, Track 3, general purpose diagnostic ultrasound system which consists of a mobile console approximately 585 mm wide (keyboard), 991 mm deep and 1300 mm high that provides digital acquisition, processing and display capability. The user interface includes a computer keyboard, specialized controls, 12-inch high-resolution color touch screen and 23.8-inch High Contrast LED LCD monitor.

The provided text describes three AI features: Auto Abdominal Color Assistant 2.0, Auto Aorta Measure Assistant, and Auto Common Bile Duct (CBD) Measure Assistant, along with a UGFF Clinical Study.

Here's an analysis of the acceptance criteria and study details for each, where available:

1. Table of Acceptance Criteria and Reported Device Performance

For Auto Abdominal Color Assistant 2.0

For Auto Aorta Measure Assistant

For Auto Common Bile Duct (CBD) Measure Assistant

For UGFF Clinical Study

2. Sample size used for the test set and the data provenance

Auto Abdominal Color Assistant 2.0:

• Sample Size: 49 individual subjects (1186 annotation images)
• Data Provenance: Retrospective, from the USA (100%).

Auto Aorta Measure Assistant:

• Sample Size:
  • Long View Aorta: 36 subjects
  • Short View Aorta: 35 subjects
• Data Provenance: Retrospective, from Japan and USA.

Auto Common Bile Duct (CBD) Measure Assistant:

• Sample Size: 25 subjects
• Data Provenance: Retrospective, from USA (40%) and Japan (60%).

UGFF Clinical Study:

• Sample Size:
  • Original study: 582 participants
  • Confirmatory study (US/EU): 15 US patients and 5 EU patients (total 20)
  • Confirmatory study (UGFF vs UDFF): 24 EU patients
• Data Provenance: Retrospective and Prospective implicitly (clinical study implies data collection).
  • Original Study: Japan (Asian population)
  • Confirmatory Study (US/EU): US and EU (demographic info unavailable for EU patients, US patients: BMI 21.0-37.5, SCD 13.9-26.9)
  • Confirmatory Study (UGFF vs UDFF): EU

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

Auto Abdominal Color Assistant 2.0:

• Number of Experts: Not specified. The text mentions "Readers to ground truth the 'anatomy'".
• Qualifications of Experts: Not specified.

Auto Aorta Measure Assistant:

• Number of Experts: Not specified. The text mentions "Readers to ground truth the AP measurement..." and an "Arbitrator to select most accurate measurement among all readers." This implies multiple readers and a single arbitrator.
• Qualifications of Experts: Not specified.

Auto Common Bile Duct (CBD) Measure Assistant:

• Number of Experts: Not specified. The text mentions "Readers to ground truth the diameter..." and an "Arbitrator to select most accurate measurement among all readers." This implies multiple readers and a single arbitrator.
• Qualifications of Experts: Not specified.

UGFF Clinical Study:

• Number of Experts: Not applicable, as ground truth was established by MRI-PDFF measurements, not expert consensus on images.

4. Adjudication method for the test set

Auto Abdominal Color Assistant 2.0:

• Adjudication Method: Not explicitly described as a specific method (e.g., 2+1). The process mentions "Readers to ground truth" and then comparison to AI predictions, but no specific adjudication among multiple readers' initial ground truths.

Auto Aorta Measure Assistant:

• Adjudication Method: Implies an arbitrator-based method. "Arbitrator to select most accurate measurement among all readers." This suggests multiple readers provide measurements, and a single arbitrator makes the final ground truth selection.

Auto Common Bile Duct (CBD) Measure Assistant:

• Adjudication Method: Implies an arbitrator-based method. "Arbitrator to select most accurate measurement among all readers." Similar to the Aorta assistant.

UGFF Clinical Study:

• Adjudication Method: Not applicable. Ground truth was established by MRI-PDFF measurements.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

Auto Abdominal Color Assistant 2.0:

• MRMC Study: Not explicitly stated as a comparative effectiveness study showing human improvement. The study focuses on the algorithm's performance against ground truth.
• Effect Size (Human Improvement with AI): Not reported.

Auto Aorta Measure Assistant:

• MRMC Study: Yes, an implicit MRMC study comparing human performance with and without AI. Readers performed measurements with and without AI assistance.
• Effect Size (Human Improvement with AI):
  • Long View Aorta (Keystrokes): Average keystrokes reduced from 4.132 (without AI) to 1.236 (with AI).
  • Short View Aorta (Keystrokes): Average keystrokes reduced from 7.05 (without AI) to 2.307 (with AI).
  • (No specific improvement in diagnostic accuracy for human readers with AI is stated, primarily focuses on efficiency via keystrokes).

Auto Common Bile Duct (CBD) Measure Assistant:

• MRMC Study: Yes, an implicit MRMC study comparing human performance with and without AI. Readers performed measurements with and without AI assistance.
• Effect Size (Human Improvement with AI):
  • Porta Hepatis CBD (Keystrokes): Average reduction in keystrokes for measurements with AI vs. manually is 1.62 +/- 0.375.
  • (No specific improvement in diagnostic accuracy for human readers with AI is stated, primarily focuses on efficiency via keystrokes).

UGFF Clinical Study:

• MRMC Study: No, this was a standalone algorithm performance study compared to a reference standard (MRI-PDFF) and a predicate device (UDFF). It did not involve human readers using the AI tool.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

Auto Abdominal Color Assistant 2.0:

• Standalone Performance: Yes. The reported accuracy, sensitivity, specificity, and DICE score are for the algorithm's performance.

Auto Aorta Measure Assistant:

• Standalone Performance: Yes, implicitly. The "AI baseline measurement" was compared for accuracy against the arbitrator-selected ground truth. While keystrokes involved human interaction to use the AI, the measurement accuracy is an algorithm output.

Auto Common Bile Duct (CBD) Measure Assistant:

• Standalone Performance: Yes, implicitly. The "AI baseline measurement" was compared for accuracy against the arbitrator-selected ground truth.

UGFF Clinical Study:

• Standalone Performance: Yes. The study directly assesses the correlation and agreement of the UGFF algorithm's output with MRI-PDFF and another ultrasound-derived fat fraction algorithm.

7. The type of ground truth used

Auto Abdominal Color Assistant 2.0:

• Ground Truth Type: Expert consensus for anatomical visibility ("Readers to ground truth the 'anatomy' visible in static B-Mode image.")

Auto Aorta Measure Assistant:

• Ground Truth Type: Expert consensus from multiple readers, adjudicated by an arbitrator, for specific measurements ("Arbitrator to select most accurate measurement among all readers.")

Auto Common Bile Duct (CBD) Measure Assistant:

• Ground Truth Type: Expert consensus from multiple readers, adjudicated by an arbitrator, for specific measurements ("Arbitrator to select most accurate measurement among all readers.")

UGFF Clinical Study:

• Ground Truth Type: Outcomes data / Quantitative Reference Standard: MRI Proton Density Fat Fraction (MRI-PDFF %).

8. The sample size for the training set

Auto Abdominal Color Assistant 2.0:

• Training Set Sample Size: Not specified beyond "The exams used for test/training validation purpose are separated from the ones used during training process".

Auto Aorta Measure Assistant:

• Training Set Sample Size: Not specified beyond "The exams used for regulatory validation purpose are separated from the ones used during model development process".

Auto Common Bile Duct (CBD) Measure Assistant:

• Training Set Sample Size: Not specified beyond "The exams used for regulatory validation purpose are separated from the ones used during model development process".

UGFF Clinical Study:

• Training Set Sample Size: Not specified. The study describes validation but not the training phase.

9. How the ground truth for the training set was established

Auto Abdominal Color Assistant 2.0:

• Training Set Ground Truth: Not explicitly detailed, but implied to be similar to the test set ground truthing process: "Information extracted purely from Ultrasound B-mode images" and "Readers to ground truth the 'anatomy'".

Auto Aorta Measure Assistant:

• Training Set Ground Truth: Not explicitly detailed, but implied to be similar to the test set ground truthing process: "Information extracted purely from Ultrasound B-mode images" and "Readers to ground truth the AP measurement...".

Auto Common Bile Duct (CBD) Measure Assistant:

• Training Set Ground Truth: Not explicitly detailed, but implied to be similar to the test set ground truthing process: "Information extracted purely from Ultrasound B-mode images" and "Readers to ground truth the diameter...".

UGFF Clinical Study:

• Training Set Ground Truth: Not specified for the training set, but for the validation set, the ground truth was MRI-PDFF measurements.

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The SIGNA™ Sprint is a whole body magnetic resonance scanner designed to support high resolution, high signal-to-noise ratio, and short scan times. It is indicated for use as a diagnostic imaging device to produce axial, sagittal, coronal, and oblique images, spectroscopic images, parametric maps, and/or spectra, dynamic images of the structures and/or functions of the entire body, including, but not limited to, head, neck, TMJ, spine, breast, heart, abdomen, pelvis, joints, prostate, blood vessels, and musculoskeletal regions of the body. Depending on the region of interest being imaged, contrast agents may be used.

The images produced by SIGNA™ Sprint reflect the spatial distribution or molecular environment of nuclei exhibiting magnetic resonance. These images and/or spectra when interpreted by a trained physician yield information that may assist in diagnosis.

SIGNA™ Sprint is a whole-body magnetic resonance scanner designed to support high resolution, high signal-to-noise ratio, and short scan time. The system uses a combination of time-varying magnet fields (Gradients) and RF transmissions to obtain information regarding the density and position of elements exhibiting magnetic resonance. The system can image in the sagittal, coronal, axial, oblique, and double oblique planes, using various pulse sequences, imaging techniques and reconstruction algorithms. The system features a 1.5T superconducting magnet with 70cm bore size. The system is designed to conform to NEMA DICOM standards (Digital Imaging and Communications in Medicine).

Key aspects of the system design:

• Uses the same magnet as a conventional whole-body 1.5T system, with integral active shielding and a zero boil-off cryostat.
• A gradient coil that achieves up to 65 mT/m peak gradient amplitude and 200 T/m/s peak slew rate.
• An embedded body coil that reduces thermal and enhance intra-bore visibility.
• A newly designed 1.5T AIR Posterior Array.
• A detachable patient table.
• A platform software with various PSD and applications, including the following AI features:
  • AIRx™ (Cleared in K183231)
  • AIR™ Recon DL (Cleared in K202238)
  • Sonic DL™ (Cleared in K223523)

The provided text is a 510(k) clearance letter and summary for a new MRI device, SIGNA™ Sprint. It states explicitly that no clinical studies were required to support substantial equivalence. Therefore, the information requested regarding acceptance criteria, study details, sample sizes, ground truth definitions, expert qualifications, and MRMC studies is not available in this document.

The document highlights the device's technical equivalence to a predicate device (SIGNA™ Premier) and reference devices (SIGNA™ Artist, SIGNA™ Champion) and relies on non-clinical tests and sample clinical images to demonstrate acceptable diagnostic performance.

Here's a breakdown of what can be extracted from the document regarding testing, and why other requested information is absent:

1. A table of acceptance criteria and the reported device performance

• Acceptance Criteria (Implicit): The document states that the device's performance is demonstrated through "bench testing and clinical testing that show the image quality performance of SIGNA™ Sprint compared to the predicate device." It also mentions "acceptable diagnostic image performance... in accordance with the FDA Guidance 'Submission of Premarket Notifications for Magnetic Resonance Diagnostic Devices' issued on October 10, 2023."
  • Specific quantitative acceptance criteria (e.g., minimum SNR, CNR, spatial resolution thresholds) are not explicitly stated in this document.
• Reported Device Performance: "The images produced by SIGNA™ Sprint reflect the spatial distribution or molecular environment of nuclei exhibiting magnetic resonance. These images and/or spectra when interpreted by a trained physician yield information that may assist in diagnosis."
  • No specific quantitative performance metrics (e.g., sensitivity, specificity, accuracy, or detailed image quality scores) are provided in this regulatory summary. The statement "The image quality of the SIGNA™ Sprint is substantially equivalent to that of the predicate device" is the primary performance claim.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Test Set Sample Size: Not applicable/Not provided. The document explicitly states: "The subject of this premarket submission, the SIGNA™ Sprint, did not require clinical studies to support substantial equivalence."
• Data Provenance: Not applicable/Not provided for a formal clinical test set. The document only mentions "Sample clinical images have been included in this submission," but does not specify their origin or nature beyond being "sample."

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

• Not applicable. Since no formal clinical study was conducted for substantial equivalence, there was no "test set" requiring ground truth established by experts in the context of an effectiveness study. The "interpretation by a trained physician" is mentioned in the Indications for Use, which is general to MR diagnostics, not specific to a study.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

• Not applicable. No clinical test set requiring adjudication was conducted for substantial equivalence.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

• No. The document explicitly states: "The subject of this premarket submission, the SIGNA™ Sprint, did not require clinical studies to support substantial equivalence." While the device incorporates AI features cleared in other submissions (AIRx™, AIR™ Recon DL, Sonic DL™), this specific 510(k) for the SIGNA™ Sprint system itself does not include an MRMC study or an assessment of human reader improvement with these integrated AI features. The focus is on the substantial equivalence of the overall MR system.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

• No, not for the SIGNA™ Sprint as a whole system. This 510(k) is for the MR scanner itself, not for a standalone algorithm. Any standalone performance for the integrated AI features (AIRx™, AIR™ Recon DL, Sonic DL™) would have been part of their respective clearance submissions (K183231, K202238, K223523), not this one.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

• Not applicable. No formal clinical study requiring ground truth was conducted for this submission.

8. The sample size for the training set

• Not applicable/Not provided. This submission is for the SIGNA™ Sprint MR system itself, not a new AI algorithm requiring a training set developed for this specific submission. The AI features mentioned (AIRx™, AIR™ Recon DL, Sonic DL™) were cleared in previous 510(k)s and would have had their own training and validation processes.

9. How the ground truth for the training set was established

• Not applicable/Not provided. As explained in point 8, this submission does not detail the training of new AI algorithms.

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The system is intended to produce cross-sectional images of the body by computer reconstruction of x-ray transmission projection data from the same axial plane taken at different angles. The system may acquire data using Axial, Cine, Helical, Cardiac, and Gated CT scan techniques from patients of all ages. These images may be obtained either with or without contrast. This device may include signal analysis and display equipment, patient and equipment supports, components and accessories.

This device may include data and image processing to produce images in a variety of trans-axial and reformatted planes. Further, the images can be post processed to produce additional imaging planes or analysis results.

The system is indicated for head, whole body, cardiac, and vascular X-ray Computed Tomography applications.

The device output is a valuable medical tool for the diagnosis of disease, trauma, or abnormality and for planning, guiding, and monitoring therapy.

If the spectral imaging option is included on the system, the system can acquire CT images using different kV levels of the same anatomical region of a patient in a single rotation from a single source. The differences in the energy dependence of the attenuation coefficient of the different materials provide information about the chemical composition of body materials. This approach enables images to be generated at energies selected from the available spectrum to visualize and analyze information about anatomical and pathological structures.

GSI provides information of the chemical composition of renal calculi by calculation and graphical display of the spectrum of effective atomic number. GSI Kidney stone characterization provides additional information to aid in the characterization of uric acid versus non-uric acid stones. It is intended to be used as an adjunct to current standard methods for evaluating stone etiology and composition.

The CT system is indicated for low dose CT for lung cancer screening. The screening must be performed within the established inclusion criteria of programs/ protocols that have been approved and published by either a governmental body or professional medical society.

This proposed device Revolution Vibe is a general purpose, premium multi-slice CT Scanning system consisting of a gantry, table, system cabinet, scanner desktop, power distribution unit, and associated accessories. It has been optimized for cardiac performance while still delivering exceptional imaging quality across the entire body.

Revolution Vibe is a modified dual energy CT system based on its predicate device Revolution Apex Elite (K213715). Compared to the predicate, the most notable change in Revolution Vibe is the modified detector design together with corresponding software changes which is optimized for cardiac imaging providing capability to image the whole heart in one single rotation same as the predicate.

Revolution Vibe offers an accessible whole heart coverage, full cardiac capability CT scanner which can deliver outstanding routine head and body imaging capabilities. The detector of Revolution Vibe uses the same GEHC's Gemstone scintillator with 256 x 0.625 mm row providing up to 16 cm of coverage in Z direction within 32 cm scan field of view, and 64 x 0.625 mm row providing up to 4 cm of coverage in Z direction within 50 cm scan field of view. The available gantry rotation speeds are 0.23, 0.28, 0.35, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, and 1.0 seconds per rotation.

Revolution Vibe inherits virtually all of the key technologies from the predicate such as: high tube current (mA) output, 80 cm bore size with Whisper Drive, Deep Learning Image Reconstruction for noise reduction (DLIR K183202/K213999, GSI DLIR K201745), ASIR-V iterative recon, enhanced Extended Field of View (EFOV) reconstruction MaxFOV 2 (K203617), fast rotation speed as fast as 0.23 second/rot (K213715), and spectral imaging capability enabled by ultrafast kilovoltage(kv) switching (K163213), as well as ECG-less cardiac (K233750). It also includes the Auto ROI enabled by AI which is integrated within the existing SmartPrep workflow for predicting Baseline and monitoring ROI automatically. As such, the Revolution Vibe carries over virtually all features and functionalities of the predicate device Revolution Apex Elite (K213715).

This CT system can be used for low dose lung cancer screening in high risk populations*.

The provided FDA 510(k) clearance letter and summary for the Revolution Vibe CT system does not include detailed acceptance criteria or a comprehensive study report to fully characterize the device's performance against specific metrics. The information focuses more on the equivalence to a predicate device and general safety/effectiveness.

However, based on the text, we can infer some aspects related to the Auto ROI feature, which is the only part of the device described with specific performance testing details.

Here's an attempt to extract and describe the available information, with clear indications of what is not provided in the document.

Acceptance Criteria and Device Performance for Auto ROI

The document mentions specific performance testing for the "Auto ROI" feature, which utilizes AI. For other aspects of the Revolution Vibe CT system, the submission relies on demonstrating substantial equivalence to the predicate device (Revolution Apex Elite) through engineering design V&V, bench testing, and a clinical reader study focused on overall image utility, rather than specific quantitative performance metrics meeting predefined acceptance criteria for the entire system.

1. Table of Acceptance Criteria and Reported Device Performance (Specific to Auto ROI)

Note: The document does not provide the explicit numerical value for the "pre-established acceptance criteria" or the actual "success rate" achieved for the Auto ROI feature.

2. Sample Size and Data Provenance for the Test Set (Specific to Auto ROI)

• Sample Size: 1341 clinical images
• Data Provenance: "real clinical practice" (Specific country of origin not mentioned). The images were used for "Auto ROI performance" testing, which implies retrospective analysis of existing clinical data.

3. Number of Experts and Qualifications to Establish Ground Truth (Specific to Auto ROI)

• Number of Experts: Not specified for the Auto ROI ground truth establishment.
• Qualifications of Experts: Not specified for the Auto ROI ground truth establishment.

Note: The document mentions 3 readers for the overall clinical reader study (see point 5), but this is for evaluating the diagnostic utility and image quality of the CT system and not explicitly for establishing ground truth for the Auto ROI feature.

4. Adjudication Method for the Test Set (Specific to Auto ROI)

• Adjudication Method: Not specified for the Auto ROI test set.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• Was an MRMC study done? Yes, a "clinical reader study of sample clinical data" was carried out. It is described as a "blinded, retrospective clinical reader study."

• Effect Size of Human Readers Improvement with AI vs. without AI assistance: The document states the purpose of this reader study was to validate that "Revolution Vibe are of diagnostic utility and is safe and effective for its intended use." It does not report an effect size or direct comparison of human readers' performance with and without AI assistance (specifically for the Auto ROI feature within the context of reader performance). The study seemed to evaluate the CT system's overall image quality and clinical utility, possibly implying that the Auto ROI is integrated into this overall evaluation, but a comparative effectiveness study of the AI's impact on human performance is not described.

  • Details of MRMC Study:
    • Number of Cases: 30 CT cardiac exams
    • Number of Readers: 3
    • Reader Qualifications: US board-certified in Radiology with more than 5 years' experience in CT cardiac imaging.
    • Exams Covered: "wide range of cardiac clinical scenarios."
    • Reader Task: "Readers were asked to provide evaluation of image quality and the clinical utility."

6. Standalone (Algorithm Only) Performance

• Was a standalone study done? Yes, for the "Auto ROI" feature, performance was tested "using 1341 clinical images from real clinical practice," and "the tests results in success rates exceeding the pre-established acceptance criteria." This implies an algorithm-only evaluation of the Auto ROI's ability to successfully identify and monitor ROI.

7. Type of Ground Truth Used (Specific to Auto ROI)

• Type of Ground Truth: Not explicitly stated for the Auto ROI. Given the "success rates" metric, it likely involved a comparison against a predefined "true" ROI determined by human experts or a gold standard method. It's plausible that this was established by expert consensus or reference standards.

8. Sample Size for the Training Set

• Sample Size: Not provided in the document.

9. How Ground Truth for the Training Set Was Established

• Ground Truth Establishment: Not provided in the document.

In summary, the provided documentation focuses on demonstrating substantial equivalence of the Revolution Vibe CT system to its predicate, Revolution Apex Elite, rather than providing detailed, quantitative performance metrics against specific acceptance criteria for all features. The "Auto ROI" feature is the only component where specific performance testing (standalone) is briefly mentioned, but key details like numerical acceptance criteria, actual success rates, and ground truth methodology for training datasets are not disclosed. The human reader study was for general validation of diagnostic utility, not a comparative effectiveness study of AI assistance.

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The Venue, Venue Go, Venue Fit and Venue Sprint are general purpose diagnostic ultrasound systems for use by qualified and trained healthcare professionals or practitioners that are legally authorized or licensed by law in the country, state or other local municipality in which he or she practices, for ultrasound imaging, measurement, display and analysis of the human body and fluid. The users may or may not be working under supervision or authority of a physician. Users may also include medical students working under the supervision or authority of a physician during their education / training.

Venue, Venue Go and Venue Fit are intended to be used in a hospital or medical clinic. Venue, Venue Go and Venue Fit clinical applications include: abdominal (GYN and Urology), thoracic/pleural, ophthalmic, Fetal/OB, Small Organ (including breast, testes, thyroid), Vascular/Peripheral vascular, neonatal and adult cephalic, pediatric, musculoskeletal (conventional and superficial), cardiac (adults and pediatric), Transrectal, Transvaginal, Transesophageal, Intraoperative (vascular) and interventional guidance (includes tissue biopsy, fluid drainage, vascular and non-vascular access). Modes of operation include: B, M, PW Doppler, CW Doppler, Color Doppler, Color M Doppler, Power Doppler, Harmonic Imaging, Coded Pulse and Combined modes: B/M, B/Color M, B/PWD, B/Color/PWD, B/Power/PWD, B/CWD, B/Color/CWD.

The Venue Sprint is intended to be used in a hospital, medical clinic, home environment and road/air ambulance. Venue Sprint clinical applications include: abdominal (GYN and Urology), thoracic/pleural, ophthalmic, Fetal/OB, Small Organ (including breast, testes, thyroid), Vascular/Peripheral vascular, neonatal and adult cephalic, pediatric, musculoskeletal (conventional and superficial), cardiac (adults and pediatric, 40 kg and above) and interventional guidance (includes free hand tissue biopsy, fluid drainage, vascular and non-vascular access). Modes of operation include: B, M, PW Doppler, Color Doppler and Harmonic Imaging.

Venue, Venue Go, Venue Fit and Venue Sprint are general-purpose diagnostic ultrasound systems intended for use by qualified and trained healthcare professionals to evaluate the body by ultrasound imaging and fluid flow analysis.

The systems utilize a variety of linear, convex, and phased array transducers which provide high imaging capability, supporting all standard acquisition modes.

The systems have a small footprint that easily fits into tight spaces and positioned to accommodate the sometimes-awkward work settings of the point of care user.

The Venue is a mobile system, the Venue Go and Venue Fit are compact, portable systems that can be hand carried using an integrated handle, placed on a horizontal surface, attached to a mobile cart or mounted on the wall. Venue, Venue Go and Venue Fit have a high-resolution color LCD monitor, with a simple, multi-touch user interface that makes the systems intuitive.

The Venue Sprint is used together with the Vscan Air probes and provides the user interface for control of the probes and the needed software functionality for analysis of the ultrasound images and saving/storage of the related images and videos.

The Venue, Venue Go, Venue Fit and Venue Sprint systems can be powered through an electrical wall outlet for long term use or from an internal battery for a short time with full functionality and scanning. A barcode reader and RFID scanner are available as additional input devices. The systems meet DICOM requirements to support users image storage and archiving needs and allows for output to printing devices.

The Venue, Venue Go and Venue Fit systems are capable of displaying the patient's ECG trace synchronized to the scanned image. This allows the user to view an image from a specific time of the ECG signal which is used as an input for gating during scanning. The ECG signal can be input directly from the patient or as an output from an ECG monitoring device. ECG information is not intended for monitoring or diagnosis. Compatible biopsy kits can be used for needle-guidance procedures.

The provided document, a 510(k) Clearance Letter and Submission Summary, primarily focuses on the substantial equivalence of the GE Healthcare Venue series of diagnostic ultrasound systems to previously cleared predicate devices. It specifically details the "Auto Bladder Volume (ABV)" feature as an AI-powered component and provides a summary of its testing.

Here's a breakdown of the acceptance criteria and the study proving the device meets them, based only on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance (for Auto Bladder Volume - ABV)

2. Sample Size Used for the Test Set and Data Provenance

• Test Set (Verification Dataset) Sample Size: 1874 images from 101 individuals.
• Data Provenance:
  • Country of Origin: USA and Israel.
  • Retrospective or Prospective: Not explicitly stated as either retrospective or prospective. However, the description of "data collected from several different Console variants" for training and verification suggests pre-existing data, which often leans towards a retrospective collection.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

• Number of Experts: Not explicitly stated. The document refers to "annotators" who performed manual annotation.
• Qualifications of Experts: Not explicitly stated. The annotators are described as performing "manual annotation," implying they are skilled in this task, but specific qualifications (e.g., radiologists, sonographers, years of experience) are not provided.

4. Adjudication Method for the Test Set

• Adjudication Method: Not explicitly stated. The document mentions "annotators performed manual annotation," but does not detail if multiple annotators were used for each case or any specific adjudication process (e.g., 2+1, 3+1 consensus).

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• Was an MRMC study done? No. The document states: "The subjects of this premarket submission, Venue, Venue Go, Venue Fit and Venue Sprint, did not require clinical studies to support substantial equivalence." The testing described for ABV is a standalone algorithm performance validation against established ground truth, not a comparative human-AI study.
• Effect Size of Human Readers Improvement: Not applicable, as no MRMC study was performed.

6. Standalone (Algorithm Only) Performance Study

• Was a standalone study done? Yes. The "AI Summary of Testing" section describes a study for the Auto Bladder Volume (ABV) feature, which assesses the algorithm's "automatic caliper placement success rate" against manually established ground truth. This is a standalone performance evaluation of the algorithm.

7. Type of Ground Truth Used (for ABV Test Set)

• Ground Truth Type: Expert consensus/manual annotation. The document states: "Ground truth annotations of the verification dataset were obtained as follows: In all Training/Validation and Verification datasets, annotators performed manual annotation on images converted from DICOM files." They identified "landmarks, which represent the bladder edges," corresponding to standard measurement locations.

8. Sample Size for the Training Set (for ABV)

• Training Set Sample Size: Total dataset included 8,392 images from 496 individuals. Of these, 1,874 were used for the verification dataset, and "the rest" were used for training/validation. This implies the training/validation set would be 8392 - 1874 = 6518 images from the remaining individuals not included in the verification set.

9. How the Ground Truth for the Training Set Was Established (for ABV)

• Ground Truth Establishment: Similar to the verification dataset, "annotators performed manual annotation on images converted from DICOM files" for both Training/Validation and Verification datasets. They chose "4-6 images that represent different bladder volume status" for each individual and annotated "4 different landmarks" per view (transverse and longitudinal) representing bladder edges.

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EchoPAC Software Only / EchoPAC Plug-in is intended for diagnostic review and analysis of ultrasound images, patient record management and reporting, for use by, or on the order of a licensed physician. EchoPAC Software Only / EchoPAC Plug-in allows post-processing of raw data images from GE ultrasound scanners and DICOM ultrasound images.

Ultrasound images are acquired via B (2D), M, Color M modes, Color, Power, Pulsed and CW Doppler modes, Coded Pulse, Harmonic, 3D, and Real time (RT) 3D Mode (4D).

Clinical applications include: Fetal/Obstetrics; Abdominal (including renal and GYN); Urology (including prostate); Pediatric; Small organs (breast, testes, thyroid); Neonatal and Adult Cephalic; Cardiac (adult and pediatric); Peripheral Vascular; Transesophageal (TEE); Musculo-skeletal Conventional; Musculo-skeletal Superficial; Transrectal (TR); Transvaginal (TV); Intraoperative (vascular); Intra-Cardiac; Thoracic/Pleural and Intra-Luminal.

EchoPAC Software Only / EchoPAC Plug-in provides image processing, annotation, analysis, measurement, report generation, communication, storage and retrieval functionality to ultrasound images that are acquired via the GE Healthcare Vivid family of ultrasound systems, as well as DICOM images from other ultrasound systems. EchoPAC Software Only will be offered as SW only to be installed directly on customer PC hardware and EchoPAC Plug-in is intended to be hosted by a generalized PACS host workstation. EchoPAC Software Only / EchoPAC Plug-in is DICOM compliant, transferring images and data via LAN between systems, hard copy devices, file servers and other workstations.

The provided 510(k) clearance letter and summary discuss the EchoPAC Software Only / EchoPAC Plug-in, including a new "AI Cardiac Auto Doppler" feature. The acceptance criteria and the study proving the device meets these criteria are primarily detailed for this AI-driven feature.

Here's an organized breakdown of the information:

1. Acceptance Criteria and Reported Device Performance (AI Cardiac Auto Doppler)

2. Sample Size and Data Provenance for the Test Set

• Sample Size:

  • Tissue Doppler: 4106 recordings from 805 individuals.
  • Doppler Trace: 3390 recordings from 1369 individuals.
  • BMI Sub-analysis: 41 patients, 433 Doppler measurements (subset of Vivid Pioneer dataset).

• Data Provenance: Retrospective, collected from standard clinical practices.

  • Countries of Origin: USA (several locations), Australia, France, Spain, Norway, Italy, Germany, Thailand, Philippines.

3. Number of Experts and Qualifications for Ground Truth

• Number of Experts:

  • Annotators: Two cardiologists.
  • Review Panel: Five clinical experts.

• Qualifications of Experts:

  • Annotators: Cardiologists, implying medical expertise in cardiac imaging and diagnosis. They followed US ASE (American Society of Echocardiography) based annotation guidelines.
  • Review Panel: Clinical experts, implying medical professionals with experience in the relevant clinical domain.

4. Adjudication Method for the Test Set

The ground truth establishment process involved:

• Two cardiologists performed initial annotations.
• A review panel of five clinical experts provided feedback on these annotations.
• Annotations were corrected (as needed) until a consensus agreement was achieved between the annotators and reviewers. This suggests an iterative consensus-based adjudication method.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• No MRMC comparative effectiveness study was explicitly mentioned. The provided document focuses on the standalone performance of the AI algorithm against expert-derived ground truth, not human-in-the-loop performance.
• Therefore, an effect size of how much human readers improve with AI vs. without AI assistance is not provided.

6. Standalone (Algorithm Only) Performance

• Yes, a standalone performance evaluation was done. The "AI Auto Doppler Summary of Testing" section describes the performance of the AI Cardiac Auto Doppler algorithm itself, without human intervention for the critical performance metrics (e.g., "All clinical parameters, as performed by AI Cardiac Auto Doppler without user edits passed this check").

7. Type of Ground Truth Used

• The ground truth was established by expert consensus (two cardiologists performing annotations, reviewed and corrected by a panel of five clinical experts until consensus).
• It was based on manual measurements and assessments of Doppler signal quality and ECG signal quality on curated images, following US ASE based annotation guidelines.

8. Sample Size for the Training Set

• Tissue Doppler: 1482 recordings from 4 unique clinical sites.
• Doppler Trace: 2070 recordings from 4 unique clinical sites.

9. How the Ground Truth for the Training Set Was Established

• The ground truth for both development (training) and verification (testing) datasets was established using the same "truthing" process:
  • Annotators (two cardiologists) performed manual measurements after assessing Doppler signal quality and ECG signal quality of curated images.
  • These annotations followed US ASE based annotation guidelines.
  • A review panel of five clinical experts provided feedback, and corrections were made until a consensus agreement was achieved between the annotators and reviewers.
• It is explicitly stated that the development dataset was selected from clinical sites not used for the testing dataset, ensuring independence between training and test data.

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Vivid Pioneer is a general-purpose ultrasound system, specialized for use in cardiac imaging. It is intended for use by, or under the direction of a qualified and trained physician or sonographer for ultrasound imaging, measurement, display and analysis of the human body and fluid.

Vivid Pioneer is intended for use in a hospital environment including echo lab, other hospital settings, operating room, Cath lab and EP lab or in private medical offices. The systems support the following clinical applications:

Fetal/Obstetrics, Abdominal (including renal, GYN), Pediatric, Small Organ (breast, testes, thyroid), Neonatal Cephalic, Adult Cephalic, Cardiac (adult and pediatric), Peripheral Vascular, Musculo-skeletal Conventional, Musculo-skeletal Superficial, Urology (including prostate), Transesophageal, Transvaginal, Transrectal, Intra-cardiac, Intra-luminal and Interventional Guidance (including Biopsy, Vascular Access), Thoracic/Pleural and Intraoperative (vascular).

Modes of operation include: 3D, Real time (RT) 3D Mode (4D), B, M, PW Doppler, CW Doppler, Color Doppler, Color M Doppler, Power Doppler, Harmonic Imaging, Coded Pulse and Combined modes: B/M, B/Color M, B/PWD or CWD, B/Color/PWD or CWD, B/Power/PWD.

The proposed Vivid Pioneer is a general purpose, Track 3, diagnostic ultrasound system, which is primarily intended for cardiac imaging and analysis but also includes vascular and general radiology applications. It provides digital acquisition, processing, display and analysis capabilities. It consists of a mobile console with a height-adjustable control panel, color LCD touch panel, and a display monitor.

Vivid Pioneer includes a variety of electronic array transducers operating in linear, curved, sector/phased array, matrix array or dual array format, including dedicated CW transducers and real time 3D transducer. The proposed Vivid Pioneer can be used with the stated compatible OEM ICE transducers. The system includes capability to output data to other devices like printing devices.

The user-interface includes an operator control panel, a 23.8" High-Definition Ultrasound LCD type of display monitor (mounted on an arm for rotation and / or adjustment of height), a layout of pre-defined user controls (hard-keys) and a 15.6-inch multi-touch LCD panel with mode-and operation dependent soft-keys.

The operator panel also includes two loudspeakers for audio, shelves for convenient placement of papers or accessories, and 6 holders with cable management for the connected transducers.

The lower console is mounted on 4 rotational wheels with brakes, for ergonomic transport and safe parking. The lower console also includes all electronics for transmit and receive of ultrasound data, ultrasound signal processing, software computing, hardware for image storage, hard copy printing, and network access to the facility through both LAN and wireless (supported by use of a wireless LAN USB-adapter) connection.

This document describes the acceptance criteria and study proving the device meets the criteria for two AI features of the Vivid Pioneer Ultrasound System: AI Cardiac Auto Doppler and AI FlexiViews LAA.

1. Table of Acceptance Criteria and Reported Device Performance

AI Cardiac Auto Doppler

AI FlexiViews LAA

2. Sample Size Used for the Test Set and Data Provenance

AI Cardiac Auto Doppler:

• Tissue Doppler test set: 4106 recordings from 805 individuals.
• Doppler Trace test set: 3390 recordings from 1369 individuals.
• Data Provenance: Retrospective, collected from USA (several locations), Australia, France, Spain, Norway, Italy, Germany, Thailand, Philippines.

AI FlexiViews LAA:

• Test set: 342 recordings from 84 individuals.
• Data Provenance: Retrospective, collected from USA, Norway, Italy, France, Philippines.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications

AI Cardiac Auto Doppler:

• Experts for annotations: Two cardiologists.
• Review panel for consensus: Five clinical experts.
• Qualifications: The document specifies "cardiologists" and "clinical experts" but does not explicitly state years of experience or board certification details.

AI FlexiViews LAA:

• Experts for annotations: Two cardiologists.
• Supervision for annotations: Two US certified clinicians.
• Review panel for consensus: Three clinical experts.
• Qualifications: The document specifies "cardiologists" and "US certified clinicians" and "clinical experts" but does not explicitly state years of experience or board certification details.

4. Adjudication Method for the Test Set

AI Cardiac Auto Doppler:

• Annotations were performed by two cardiologists.
• A review panel of five clinical experts provided feedback.
• Annotations were corrected (as needed) until a consensus agreement was achieved between the annotators and reviewers. This suggests an adjudication method aimed at reaching a single agreed-upon ground truth.

AI FlexiViews LAA:

• Annotations were performed by two cardiologists, supervised by two US certified clinicians.
• A review panel of three clinical experts provided feedback.
• Annotations were corrected (as needed) until a consensus agreement was achieved between the annotators and reviewers. Similar to Auto Doppler, this indicates a consensus-based adjudication.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

The provided text does not mention a multi-reader multi-case (MRMC) comparative effectiveness study to assess how much human readers improve with AI vs. without AI assistance for either AI Cardiac Auto Doppler or AI FlexiViews LAA. The evaluation focused on the standalone performance of the AI algorithms against expert-derived ground truth.

6. Standalone Performance (Algorithm Only)

Yes, standalone (algorithm only without human-in-the-loop performance) studies were done for both AI features.

• AI Cardiac Auto Doppler: Performance was evaluated based on the AI algorithm's measurements directly compared to expert-derived ground truth. The verification explicitly states "AI Cardiac Auto Doppler without user edits passed this check."
• AI FlexiViews LAA: The "model achieved a verification success rate of 85%" based on its localization and landmark extraction, directly reflecting standalone performance.

7. Type of Ground Truth Used

Expert Consensus.

For both AI Cardiac Auto Doppler and AI FlexiViews LAA, the ground truth was established through:

• Manual measurements/annotations performed by cardiologists.
• Assessment of Doppler/ECG signal quality.
• Supervision by US certified clinicians (for LAA).
• Review and consensus agreement among a panel of clinical experts.

8. Sample Size for the Training Set

AI Cardiac Auto Doppler:

• Tissue Doppler development dataset: 1482 recordings from 4 unique clinical sites.
• Doppler Trace development dataset: 2070 recordings from 4 unique clinical sites.

AI FlexiViews LAA:

• Total development dataset: 612 recordings from 5 unique clinical sites.

9. How the Ground Truth for the Training Set Was Established

The ground truth for the development (training/validation) datasets was established in the same manner as the ground truth for the test sets:

• For both AI Cardiac Auto Doppler and AI FlexiViews LAA:
  • Annotators (cardiologists, supervised by US certified clinicians for LAA) performed manual measurements/annotations after assessing image quality (Doppler signal quality and ECG signal quality for Auto Doppler, LAA contour and specific points for FlexiViews LAA).
  • Annotations followed US ASE (American Society of Echocardiography) based annotation guidelines.
  • A review panel of clinical experts (five for Auto Doppler, three for FlexiViews LAA) provided feedback.
  • Annotations were corrected (as needed) until a consensus agreement was achieved between the annotators and reviewers.

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The monitor B105M, B125M, B155M, B105P and B125P are portable multi-parameter patient monitors intended to be used for monitoring, recording, and to generate alarms for multiple physiological parameters of adult, pediatric, and neonatal patients in a hospital environment and during intra-hospital transport.

The monitor B105M, B125M, B155M, B105P and B125P are intended for use under the direct supervision of a licensed health care practitioner.

The monitor B105M, B125M, B155M, B105P and B125P are not Apnea monitors (i.e., do not rely on the device for detection or alarm for the cessation of breathing). These devices should not be used for life sustaining/supporting purposes.

The monitor B105M, B125M, B155M, B105P and B125P are not intended for use during MRI.

The monitor B105M, B125M, B155M, B105P and B125P can be stand-alone monitors or interfaced to other devices via network.

The monitor B105M, B125M, B155M, B105P and B125P monitor and display: ECG (including ST segment, arrhythmia detection, ECG diagnostic analysis and measurement), invasive blood pressure, heart/pulse rate, oscillometric non-invasive blood pressure (systolic, diastolic and mean arterial pressure), functional oxygen saturation (SpO2) and pulse rate via continuous monitoring (including monitoring during conditions of clinical patient motion or low perfusion), temperature with a reusable or disposable electronic thermometer for continual monitoring Esophageal/Nasopharyngeal/Tympanic/Rectal/Bladder/Axillary/Skin/Airway/Room/Myocardial/Core/Surface temperature, impedance respiration, respiration rate, airway gases (CO2, O2, N2O, anesthetic agents, anesthetic agent identification and respiratory rate), Cardiac Output (C.O.), Entropy, neuromuscular transmission (NMT) and Bispectral Index (BIS).

The monitor B105M, B125M, B155M, B105P and B125P are able to detect and generate alarms for ECG arrhythmias: Asystole, Ventricular tachycardia, VT>2, Ventricular Bradycardia, Accelerated Ventricular Rhythm, Ventricular Couplet, Bigeminy, Trigeminy, "R on T", Tachycardia, Bradycardia, Pause, Atrial Fibrillation, Irregular, Multifocal PVCs, Missing Beat, SV Tachy, Premature Ventricular Contraction (PVC), Supra Ventricular Contraction (SVC) and Ventricular fibrillation.

The proposed monitors B105M, B125M, B155M, B105P and B125P are new version of multi-parameter patient monitors developed based on the predicate monitors B105M, B125M, B155M, B105P and B125P (K213490) to provide additional monitored parameter Bispectral Index (BIS) by supporting the additional optional E-BIS module (K052145) which used in conjunction with Covidien BISx module (K072286).

In addition to the added parameter, the proposed monitors also offer below several enhancements:

• Provided data connection with GE HealthCare anesthesia devices to display the parameters measured from anesthesia devices (Applicable for B105M, B125M and B155M).
• Modified Early Warning Score calculation provided.
• Separated low priority alarms user configurable settings from the combined High/Medium/Low priority options.
• Provided additional customized notification tool to allow clinician to configure the specific notification condition of one or more physiological parameters measured by the monitor. (Applicable for B105M, B125M and B155M).
• Enhanced User Interface in Neuromuscular Transmission (NMT), Respiration Rate and alarm overview.
• Provided Venous Stasis to assist venous catheterization with NIBP cuff inflation.
• Supported alarm light brightness adjustment.
• Supported alarm audio pause by gesture (Not applicable for B105M and B105P).
• Supported automatic screen brightness adjustment.
• Supported network laser printing.
• Continuous improvements in cybersecurity

The proposed monitors B105M, B125M, B155M, B105P and B125P retain equivalent hardware design based on the predicate monitors and removal of the device Trim-knob to better support cleaning and disinfecting while maintaining the same primary function and operation.

Same as the predicate device, the five models (B105M, B125M, B155M, B105P and B125P) share the same hardware platform and software platform to support the data acquisition and algorithm modules. The differences between them are the LCD screen size and configuration options. There is no change from the predicate in the display size.

As with the predicate monitors B105M, B125M, B155M, B105P and B125P (K213490), the proposed monitors B105M, B125M, B155M, B105P and B125P are multi-parameter patient monitors, utilizing an LCD display and pre-configuration basic parameters: ECG, RESP, NIBP, IBP, TEMP, SpO2, and optional parameters which include CO2 and Gas parameters provided by the E-MiniC module (K052582), CARESCAPE Respiratory modules E-sCO and E-sCAiO (K171028), Airway Gas Option module N-CAiO (K151063), Entropy parameter provided by the E-Entropy module (K150298), Cardiac Output parameter provided by the E-COP module (K052976), Neuromuscular Transmission (NMT) parameter provided by E-NMT module (K051635) and thermal recorder B1X5-REC.

The proposed monitors B105M, B125M, B155M, B105P and B125P are not Apnea monitors (i.e., do not rely on the device for detection or alarm for the cessation of breathing). These devices should not be used for life sustaining/supporting purposes. Do not attempt to use these devices to detect sleep apnea.

As with the predicate monitors B105M, B125M, B155M, B105P and B125P (K213490), the proposed monitors B105M, B125M, B155M, B105P and B125P also can interface with a variety of existing central station systems via a cabled or wireless network which implemented with identical integrated WiFi module. (WiFi feature is disabled in B125P/B105P).

Moreover, same as the predicate monitors B105M, B125M, B155M, B105P and B125P (K213490), the proposed monitors B105M, B125M, B155M, B105P and B125P include features and subsystems that are optional or configurable, and it can be mounted in a variety of ways (e.g., shelf, countertop, table, wall, pole, or head/foot board) using existing mounting accessories.

The provided FDA 510(k) clearance letter and summary for K242562 (Monitor B105M, Monitor B125M, Monitor B155M, Monitor B105P, Monitor B125P) do not contain information about specific acceptance criteria, reported device performance metrics, or details of a study meeting those criteria for any of the listed physiological parameters or functionalities (e.g., ECG or arrhythmia detection).

Instead, the documentation primarily focuses on demonstrating substantial equivalence to a predicate device (K213490) by comparing features, technology, and compliance with various recognized standards and guidance documents for safety, EMC, software, human factors, and cybersecurity.

The summary explicitly states: "The subject of this premarket submission, the proposed monitors B105M/B125M/B155M/B105P/B125P did not require clinical studies to support substantial equivalence." This implies that the changes introduced in the new device versions were not considered significant enough to warrant new clinical performance studies or specific quantitative efficacy/accuracy acceptance criteria beyond what is covered by the referenced consensus standards.

Therefore, I cannot provide the requested information from the given text:

1. A table of acceptance criteria and the reported device performance: This information is not present. The document lists numerous standards and tests performed, but not specific performance metrics or acceptance thresholds.
2. Sample size used for the test set and the data provenance: Not explicitly stated for performance evaluation, as clinical studies were not required. The usability testing mentioned a sample size of 16 US clinical users, but this is for human factors, not device performance.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable, as detailed performance studies requiring expert ground truth are not described.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable.
5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance: Not applicable. This device is a patient monitor, not an AI-assisted diagnostic tool that would typically involve human readers.
6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done: The document describes "Bench testing related to software, hardware and performance including applicable consensus standards," which implies standalone testing against known specifications or simulated data. However, specific results or detailed methodologies for this type of testing are not provided beyond the list of standards.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not explicitly stated for performance assessment. For the various parameters (ECG, NIBP, SpO2, etc.), it would typically involve reference equipment or validated methods as per the relevant IEC/ISO standards mentioned.
8. The sample size for the training set: Not applicable, as this is not an AI/ML device that would require explicit training data in the context of this submission.
9. How the ground truth for the training set was established: Not applicable.

In summary, the provided document focuses on demonstrating that the new monitors are substantially equivalent to their predicate through feature comparison, adherence to recognized standards, and various non-clinical bench tests (e.g., hardware, alarms, EMC, environmental, reprocessing, human factors, software, cybersecurity). It does not contain the detailed performance study results and acceptance criteria typically found for novel diagnostic algorithms or AI-driven devices.

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CardIQ Suite is a non-invasive software application designed to provide an optimized application to analyze cardiovascular anatomy and pathology based on 2D or 3D CT cardiac non contrast and angiography DICOM data from acquisitions of the heart. It provides capabilities for the visualization and measurement of vessels and visualization of chamber mobility. CardIQ Suite also aids in diagnosis and determination of treatment paths for cardiovascular diseases to include, coronary artery disease, functional parameters of the heart, heart structures and follow-up for stent placement, bypasses and plaque imaging. CardIQ Suite provides calcium scoring, a non-invasive software application, that can be used with non-contrasted cardiac images to evaluate calcified plaques in the coronary arteries, heart valves and great vessels such as the aorta. The clinician can use the information provided by calcium scoring to monitor the progression/regression of calcium in coronary arteries overtime, and this information may aid the clinician in their determination of the prognosis of cardiac disease. CardIQ Suite also provides an estimate of the volume of heart fat for informational use.

CardIQ Suite is a non-invasive software application designed to work with DICOM CT data acquisitions of the heart. It is a collection of tools that provide capabilities for generating measurements both automatically and manually, displaying images and associated measurements in an easy-to-read format and tools for exporting images and measurements in a variety of formats.

CardIQ Suite provides an integrated workflow to seamlessly review calcium scoring and coronary CT angiography (CCTA) data. Calcium Scoring has a fully automatic capability which will detect calcifications within the coronary arteries, label the coronary arteries according to regional territories and generate a total and per territory calcium score based on the AJ 130 and Volume scoring methods. Interactive tools allow editing of both the auto scored coronary lesions and other calcified lesions such as aortic valve, mitral valve as well as other general cardiac structures. Calcium scoring results can be compared with two percentile guide databases to better understand a patient's percentage of risk based on age, gender, and ethnicity. Additionally, for these non-contrasted exams, the heart fat estimation automatically estimates values within the heart that constitute adipose tissue, typically between –200 and –30 Hounsfield Units.

Calcium Scoring results can be exported as DICOM SR, batch axial SCPT, or a PDF report to assist with integration into structured reporting templates. Images can be saved and exported for sharing with referring physicians, incorporating into reports and archiving as part of the CT examination.

The Multi-Planar Reformat (MPR) Cardiac Review and Coronary Review steps provide an interactive toolset for review of cardiac exams. Coronary CTA datasets can be reviewed utilizing the double oblique angles to visually track the path of the coronary arteries as well as to view the common cardiac chamber orientations. Cine capability for multi-phase data may be useful for visualization of cardiac structures in motion such as chambers, valves and arteries, automatic tracking and labeling will allow a comprehensive analysis of the coronaries. Vessel lumen diameter is calculated, and the minimum lumen diameter computed is shown in color along the lumen profile.

Distance measurement and ROI tools are available for quantitative evaluation of the anatomy. Vascular findings of interest can be identified and annotated by the user, and measurements can be calculated for centerline distances, cross-sectional diameter and area, and lumen minimum diameter.

Let's break down the acceptance criteria and study details for the CardIQ Suite device based on the provided FDA 510(k) clearance letter.

1. Table of Acceptance Criteria and Reported Device Performance

The document provides specific acceptance criteria and performance results for the novel or modified algorithms introduced in the CardIQ Suite.

2. Sample Sizes Used for the Test Set and Data Provenance

• Heart Segmentation (non-contrast CT exams): 111 CT exams
• Heart Fat Volume Estimate: 111 CT exams
• Lumen Diameter Quantification: 94 CT exams with a total of 353 narrowings across all available test sets.
• Coronary Centerline Tracking and Labeling: "a database of retrospective CT exams." (Specific number not provided for this particular test, but it is part of the overall bench testing.)

Data Provenance: The document states that the CT exams used for bench testing were "collected from different clinical sites, with a variety of acquisition parameters, and pathologies." It also notes that this database is "retrospective." The country of origin is not explicitly stated in the provided text.

3. Number of Experts Used to Establish Ground Truth and Qualifications

The document does not explicitly state the number of experts used or their specific qualifications (e.g., radiologist with 10 years of experience) for establishing the ground truth for the test sets. The tests are described as "bench testing" and comparisons to a "reference device" (CardIQ Xpress 2.0) or to an expectation of "successfully segmented."

4. Adjudication Method for the Test Set

The document does not explicitly describe an adjudication method (e.g., 2+1, 3+1). The performance is reported based on comparisons to a reference device or meeting a quantitative metric (e.g., Dice score, successful segmentation percentage, mean absolute difference).

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

The document does not mention or describe that a multi-reader multi-case (MRMC) comparative effectiveness study was done. The focus is on the performance of the algorithms themselves ('bench testing') and their enhancement compared to predicates, rather than human reader improvement with AI assistance.

6. Standalone (Algorithm Only Without Human-in-the-Loop) Performance

Yes, the studies described are standalone performance evaluations of the algorithms. They are referred to as "bench testing" and evaluate the device's algorithms directly against defined metrics or a reference device, without involving human readers in a diagnostic setting for performance comparison.

7. Type of Ground Truth Used

The type of ground truth used varies based on the specific test:

• Heart Segmentation (non-contrast CT exams) & Heart Fat Volume Estimate: The ground truth for these appears to be implicitly established by what constitutes "successfully segmented" or against which the "Dice score" is calculated. A "predefined HU threshold" is mentioned for heart fat, suggesting a quantitative, rule-based ground truth related to Hounsfield Units within segmented regions.
• Lumen Diameter Quantification: The ground truth for this was established by comparison to diameters from the reference device, CardIQ Xpress 2.0 (K073138).
• Coronary Centerline Tracking and Labeling: The ground truth for evaluating enhancement compared to the predicate is not explicitly defined but would likely involve some form of expert consensus or highly accurate manual delineation, which is then used to assess the "enhancement" of the new algorithm.

8. Sample Size for the Training Set

The document does not provide the sample size for the training set. It only mentions that the "new deep learning algorithm for heart segmentation of non-contrasted exams uses the same model as the previous existing heart segmentation algorithm for contrasted exams, however now the input is changed, and the model is trained and tested with the non-contrasted exams." Similarly for coronary tracking, it states the deep learning algorithm was "retrained to a finer resolution." However, no specific training set sizes are given.

9. How the Ground Truth for the Training Set Was Established

The document does not explicitly state how the ground truth for the training set was established. It is noted that the models were "trained," which implies the existence of a ground truth for the training data, but the methodology for its establishment (e.g., expert annotation, semi-automated methods) is not described in the provided text.

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The Aurora system is a medical tool intended for use by appropriately trained healthcare professionals to aid detecting, localizing, diagnosing of diseases and in the assessment of organ function for the evaluation of diseases, trauma, abnormalities, and disorders such as, but not limited to, cardiovascular disease, neurological disorders and cancer. The system output can also be used by the physician for staging and restaging of tumors; and planning, guiding, and monitoring therapy, including the nuclear medicine part of theragnostic procedures.

GEHC's Aurora is a SPECT-CT system that combines an all-purpose Nuclear Medicine imaging system and the commercially available Revolution Ascend system. It is intended for general purpose Nuclear Medicine imaging procedures as well as head, whole body, cardiac and vascular CT applications and CT-based corrections and anatomical localization of SPECT images. Aurora does not introduce any new Intended Use.

Aurora consists of two back-to-back gantries (i.e. one for the NM sub-system and another for the CT subsystem), patient table, power distribution unit (PDU), operator console with a computer for both the NM acquisition and SmartConsole software and another for the CT software, interconnecting cables, and associated accessories (e.g. NM collimator carts, cardiac trigger monitor, head holder). The CT sub-system main components include the CT gantry, PDU, and CT operator console. All components are from the commercially available GEHC Revolution Ascend CT system.

Here's a breakdown of the acceptance criteria and study details for the Aurora system's deep-learning Automatic Kidney Segmentation algorithm, based on the provided FDA 510(k) clearance letter:

Acceptance Criteria and Reported Device Performance

Study Details for Deep-Learning Automatic Kidney Segmentation Algorithm

1. Sample sized used for the test set and the data provenance:
* Sample Size: 70 planar NM renal studies.
* Data Provenance: Acquired using GEHC systems from:
* 2 hospitals in the United States
* 1 hospital in Europe
* Nature: Retrospective (the studies were "segregated, and not used in any stage of the algorithm development," implying they were pre-existing data).
* Diversity: Served a diverse patient population including a range of ethnicities and demographics, encompassing a range of dynamic renal clinical scenarios, detection technologies, collimators, tracers, scan parameters, and patient age.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
* Number of Experts for Bench Testing Ground Truth: One (1).
* Qualifications: "An experienced Nuclear Medicine physician."
* Number of Experts for Clinical Testing Evaluation: Three (3) qualified U.S. readers.
* Qualifications: "Qualified U.S. readers" (further specific qualifications like years of experience or board certification are not detailed).

3. Adjudication method for the test set:
* For Bench Testing Ground Truth: The ground truth contours were reviewed and confirmed by a single experienced Nuclear Medicine physician. This suggests a form of expert consensus, but without multiple experts, it's not a multi-expert adjudication like 2+1 or 3+1. It's best described as single expert confirmation.
* For Clinical Testing: The three qualified U.S. readers independently assessed the quality of segmentation using a 4-point Likert scale. There is no mention of an adjudication process among these three readers, implying their individual assessments contributed to the overall evaluation.

4. If a multi-reader multi-case (MRMC) comparative effectiveness study was done:
* No, a multi-reader multi-case (MRMC) comparative effectiveness study comparing human readers with AI assistance vs. without AI assistance was not explicitly described.
* The clinical testing involved multiple readers evaluating the quality of the algorithm's segmentation itself, rather than assessing their own diagnostic performance with and without AI. The focus was on the utility and acceptability of the AI output for the readers.

5. Effect size of how much human readers improve with AI vs without AI assistance:
* This information is not provided as a comparative effectiveness study was not explicitly conducted. The study assessed the acceptability of the AI's output, not the improvement in human reader performance.

6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:
* Yes, a standalone performance evaluation of the algorithm was done. This is described as "Bench Testing" where the algorithm's generated contours were compared directly against the ground truth (GT) contours using the DICE similarity score. The "clinical testing" involved human readers evaluating the AI output, but the bench testing was algorithm-only.

7. The type of ground truth used:
* Expert Consensus: The ground truth for the bench testing (GT contours) was established by an "experienced Nuclear Medicine physician." While only one physician is mentioned, it's considered an expert-derived ground truth.

8. The sample size for the training set:
* The document does not explicitly state the sample size used for the training set of the deep learning algorithm. It only mentions that the 70 test studies "were segregated, and not used in any stage of the algorithm development," which implies they were distinct from the training data.

9. How the ground truth for the training set was established:
* The document does not explicitly state how the ground truth for the training set was established. It is only mentioned for the test set.

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