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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 Deep Learning Image Reconstruction software is a deep learning based reconstruction method intended to produce cross-sectional images of the head and whole body by computer reconstruction of X-ray transmission data taken at different angles and planes, including Axial, (Volumetric), and Cardiac acquisitions, for all ages.

Deep Learning Image Reconstruction software can be used for head, whole body, cardiac, and vascular CT applications.

Deep Learning Image Reconstruction is an image reconstruction method that uses a dedicated Deep Neural Network (DNN) that has been designed and trained specifically to generate CT Images to give an image appearance, as shown on axial NPS plots, similar to traditional FBP images while maintaining the performance of ASiR-V in the following areas: image noise (pixel standard deviation), low contrast detectability, high-contrast spatial resolution, and streak artifact suppression.

The images produced are branded as "TrueFidelity™ CT Images". Reconstruction times with Deep Learning Image Reconstruction support a normal throughput for routine CT.

The deep learning technology is integrated into the scanner's existing raw data-based image reconstruction chain to produce DICOM compatible "TrueFidelity™ CT Images".

The system allows user selection of three strengths of Deep Learning Image Recon: Low, Medium or High. The strength selection will vary with individual users' preference for the specific clinical need.

The DLR algorithm was modified on the Revolution CT/Apex platform for improved reconstruction speed and image quality and cleared in February 2022 with K213999. The same modified DLIR is now being ported to Revolution EVO (K131576) /Revolution Maxima (K192686), Revolution Ascend (K203169, K213938) and Discovery CT750 HD family CT systems including Discovery CT750 HD, Revolution Frontier and Revolution Discovery CT (K120833).

The provided text describes that the Deep Learning Image Reconstruction software was tested for substantial equivalence to a predicate device (K213999). The study performed was largely an engineering bench testing, comparing various image quality metrics between images reconstructed with Deep Learning Image Reconstruction (DLIR) and ASiR-V from the same raw datasets.

Here's a breakdown of the requested information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The text indicates that the device aims to maintain the performance of ASiR-V in specific areas while offering an image appearance similar to traditional FBP images. The "acceptance criteria" can be inferred from the list of image quality metrics evaluated, with the performance goal being comparable or improved relative to ASiR-V.

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

• Test Set Sample Size: The text states "the identical raw datasets obtained on GEHC's Revolution Ascend, Revolution Frontier and Discovery CT750 HD systems". However, the number of cases or specific sample size for these datasets is not explicitly stated.
• Data Provenance: The raw datasets were "obtained on GEHC's Revolution Ascend, Revolution Frontier and Discovery CT750 HD systems". The country of origin is not specified, and it is stated that the study used retrospective raw datasets (i.e., existing data, not newly acquired data for the study).

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

The provided text focuses on engineering bench testing and image quality metrics. It does not mention the use of experts to establish ground truth for the test set or their qualifications. The evaluation primarily relies on quantitative image quality metrics.

4. Adjudication Method for the Test Set

Since experts were not explicitly used to establish ground truth, there is no mention of an adjudication method for the test set in the provided text.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and its effect size

An MRMC comparative effectiveness study was not performed according to the provided text. The study focused on technical image quality comparisons at the algorithm level, not human reader performance with or without AI assistance.

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

Yes, a standalone performance evaluation was done. The study described is primarily a standalone evaluation of the algorithm's image quality output (e.g., noise, resolution, artifacts, detectability) when compared to images reconstructed with ASiR-V from the same raw data.

7. The Type of Ground Truth Used

The "ground truth" for the test set was essentially:

• Quantitative Image Quality Metrics: Performance relative to ASiR-V for metrics like image noise, LCD, spatial resolution, streak artifact suppression, CT uniformity, CT number accuracy, CNR, spatial resolution (longitudinal), NPS, and artifact analysis.
• Reference Image Appearance: The stated goal was an image appearance similar to traditional FBP images shown on axial NPS plots.

There is no mention of pathology, expert consensus on clinical findings, or outcomes data being used as ground truth for this particular substantial equivalence study.

8. The Sample Size for the Training Set

The text states that the Deep Neural Network (DNN) is "trained on the CT scanner" and models the scanned object using "information obtained from extensive phantom and clinical data." However, the specific sample size for the training set is not provided.

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

The ground truth for the training set is implicitly established through the "extensive phantom and clinical data" mentioned as being used to train the DNN. The text indicates the DNN is trained to model noise propagation and identify noise characteristics to remove it, and to generate images with an appearance similar to traditional FBP while maintaining ASiR-V performance. This suggests the training involves learning from "ground truth" as defined by:

• Reference Image Quality: Likely images reconstructed with proven methods (e.g., FBP, ASiR-V) or images from phantoms with known properties.
• Noise Characteristics: The DNN is trained to understand and model noise.

However, the specific methodology for establishing this ground truth for the training data (e.g., expert annotation, simulated data, pathology confirmed disease) is not detailed in the provided text.

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The Deep Learning Image Reconstruction software is a deep learning based reconstruction method intended to produce cross-sectional images of the head and whole body by computer reconstruction of X-ray transmission data taken at different angles and planes, including Axial, Helical (Volumetric), and Cardiac acquisitions, for all ages. Deep Learning Image Reconstruction software can be used for head, whole body, cardiac, and vascular CT applications.

Deep Learning Image Reconstruction is an image reconstruction method that uses a dedicated Deep Neural Network (DNN) that has been designed and trained specifically to generate CT Images to give an image appearance, as shown on axial NPS plots, similar to traditional FBP images while maintaining the performance of ASiR-V in the following areas: dose, image noise (pixel standard deviation), low contrast detectability, high-contrast spatial resolution, and streak artifact suppression.

The images produced are branded as "TrueFidelity™ CT Images". Reconstruction times with Deep Learning Image Reconstruction software support a normal throughput for routine CT.

The deep learning technology is integrated into the scanner's existing raw data-based image reconstruction chain to produce DICOM compatible "TrueFidelity™ CT Images".

The system allows user selection of three strengths of Deep Learning Image Recon: Low, Medium or High. The strength selection will vary with individual users' preference for the specific clinical need.

The provided text is a 510(k) summary for the GE Healthcare Japan Corporation's "Deep Learning Image Reconstruction" device. It outlines the device's technical characteristics, intended use, and comparison to predicate devices for substantial equivalence determination. However, it does not include detailed information regarding specific acceptance criteria, a comprehensive study proving the device meets these criteria, or specific performance metrics in a tabular format. The document focuses on establishing substantial equivalence based on the fundamental technology being unchanged from the predicate and successful completion of design control testing and quality assurance measures.

Therefore, I cannot extract all the requested information. Here's what can be inferred and what is missing:

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

This information is not provided in the document. The document states: "Design verification and validation, including IQ bench testing, demonstrate that the Deep Learning Image Reconstruction (DLIR) software met all of its design requirement and performance criteria." However, it does not specify what those "design requirement and performance criteria" are or the reported performance data against them.

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

This information is not provided in the document. The document mentions "IQ bench testing" and "System Testing" including "Image Performance Testing (Verification)" and "Simulating Use Testing (Validation)," but does not detail the sample sizes or data provenance used for these tests.

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)

This information is not provided in the document.

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

This information is not provided in the document.

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

This information is not provided in the document. The document describes the device as a "deep learning based reconstruction method" that produces images with "similar to traditional FBP images while maintaining the performance of ASiR-V in the following areas: dose, image noise (pixel standard deviation), low contrast detectability, high-contrast spatial resolution, and streak artifact suppression." This implies a comparison to other reconstruction methods, but not a MRMC study involving human readers with and without AI assistance.

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

Yes, based on the description, the primary testing described is "standalone" algorithm performance. The device is a "deep learning based reconstruction method" and the testing described, such as "IQ bench testing" and "Image Performance Testing," focuses on the intrinsic image quality outputs of the algorithm. There is no mention of human-in-the-loop performance in the context of effectiveness studies.

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

This information is not explicitly stated in the document. Given the context of "IQ bench testing" and performance metrics like "image noise," "low contrast detectability," and "spatial resolution," it's highly likely that objective phantom studies and potentially established image quality metrics (which could be considered a form of "ground truth" for image quality, validated against known physical properties) were used. However, expert consensus on clinical diagnostic accuracy or pathology is not mentioned as a ground truth.

8. The sample size for the training set

This information is not provided in the document. It mentions that the device "uses a dedicated Deep Neural Network (DNN) that has been designed and trained specifically to generate CT Images," but the details of the training set are not disclosed.

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

This information is not provided in the document. While it states the DNN was "trained specifically to generate CT Images to give an image appearance, as shown on axial NPS plots, similar to traditional FBP images while maintaining the performance of ASiR-V," the method for establishing the ground truth for this training is not detailed.

Ask a specific question about this device

The Deep Learning Image Reconstruction software is a deep learning based reconstruction method intended to produce cross-sectional images of the head and whole body by computer reconstruction of X-ray transmission data taken at different angles and planes, including Axial, Helical (Volumetric), and Cardiac acquisitions, for all ages. Deep Learning Image Reconstruction software can be used for head, whole body, cardiac, and vascular CT applications.

Deep Learning Image Reconstruction is an image reconstruction method that uses a dedicated Deep Neural Network (DNN) that has been designed and trained specifically to generate CT Images to give an image appearance, as shown on axial NPS plots, similar to traditional FBP images while maintaining the performance of ASiR-V in the following areas: dose, image noise (pixel standard deviation), low contrast detectability, high-contrast spatial resolution, and streak artifact suppression.

The images produced are branded as "TrueFidelity"" CT Images". Reconstruction times with Deep Learning Image Reconstruction software support a normal throughput for routine CT.

The deep learning technology is integrated into the scanner's existing raw data-based image reconstruction chain to produce DICOM compatible "TrueFidelity"™ CT Images".

The system allows user selection of three strengths of Deep Learning Image Recon: Low, Medium or High. The strength selection will vary with individual users' preferences and experience for the specific clinical need.

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

Acceptance Criteria and Device Performance

The core of the acceptance criteria revolves around demonstrating that the Deep Learning Image Reconstruction (DLIR) on the Revolution Ascend system is substantially equivalent to its predicate device (DLIR on Revolution EVO) and performs at least as well as, or better than, ASiR-V reconstruction in key image quality metrics.

Study Details

1. Sample size used for the test set and the data provenance:

   • Sample Size: A total of 60 retrospectively collected clinical cases were used.
   • Data Provenance: The data was retrospectively collected. The country of origin is not explicitly stated in the provided text.

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

   • Number of Experts: 9 board-certified radiologists.
   • Qualifications: These radiologists had "expertise in the specialty areas that align with the anatomical region of each case."

3. Adjudication method for the test set:

   • Each image was read by 3 different radiologists.
   • The readers completed their evaluations independently and were blinded to the results of the other readers' assessments.
   • The text doesn't explicitly state an adjudication method like 2+1 or 3+1 for discrepancies. It implies a consensus or agreement was sought, or that individual assessments contributed to the overall conclusion of substantial equivalence. Given they provided an assessment on a Likert scale and then compared images, it seems individual reader assessments were aggregated, rather than a discrepancy resolution process.

4. 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:

   • Yes, an MRMC study was implicitly done, as 9 radiologists evaluated 60 cases, with each case being read by 3 different radiologists. The study involved a comparison between ASiR-V reconstructions and Deep Learning Image Reconstruction (DLIR) images.
   • Effect Size: The document does not provide a specific effect size (e.g., percentage improvement in accuracy or AUC) of how much human readers improved with DLIR assistance compared to ASiR-V. It states that the study results "support substantial equivalence and performance claims" and that readers assessed image quality and compared noise texture and sharpness, implying favorable or equivalent performance.

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

   • Yes, extensive standalone (algorithm only) non-clinical engineering bench testing was performed. This included evaluations of:
     • Low contrast detectability (LCD)
     • Image Noise (pixel standard deviation)
     • High contrast spatial resolution (MTF)
     • Streak Artifact Suppression
     • Spatial Resolution
     • Noise Power Spectrum (NPS) and Standard Deviation of noise
     • CT Number Accuracy and Uniformity
     • Contrast to Noise (CNR) ratio
     • Artifact analysis - metal objects, unintended motion, truncation
     • Pediatric Image Quality Performance
     • Low Dose Lung Cancer Screening

6. The type of ground truth used:

   • For the clinical reader study, the ground truth was based on expert assessment/consensus (implying the "gold standard" for diagnostic image quality, noise texture, and sharpness was the radiologists' expert opinion). The cases were "retrospectively collected clinical cases," suggesting the presence of a known clinical diagnosis or outcome, but the specific ground truth for disease presence/absence is not explicitly stated as the primary output of the DLIR evaluation. The evaluation focused more on image quality attributes and comparison between reconstruction methods rather than diagnostic accuracy against a separate definitive truth.

7. The sample size for the training set:

   • The document states the Deep Neural Network (DNN) was "trained on the Revolution family CT Scanners" but does not provide the specific sample size (number of images or cases) used for training.

8. How the ground truth for the training set was established:

   • The text does not explicitly detail how the ground truth for the training set was established. It mentions the DNN was "designed and trained specifically to generate CT Images to give an image appearance... similar to traditional FBP images while maintaining the performance of ASiR-V in the following areas: dose, image noise (pixel standard deviation), low contrast detectability, high-contrast spatial resolution, and streak artifact suppression." This suggests the training likely involved pairing raw CT data with expertly reconstructed ASiR-V or FBP images as a reference for image quality characteristics. The ground truth in this context would be the desired output image characteristics (e.g., low noise, high resolution) that the DLIR algorithm was optimized to reproduce.

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The Deep Learning Image Reconstruction software is a deep learning based reconstruction method intended to produce cross-sectional images of the head and whole body by computer reconstruction of X-ray transmission data taken at different angles and planes, including Axial, Helical (Volumetric), and Cardiac acquisitions, for all ages. Deep Learning Image Reconstruction software can be used for head, whole body, cardiac, and vascular CT applications.

Deep Learning Image Reconstruction is an image reconstruction method that uses a dedicated Deep Neural Network (DNN) that has been designed and trained specifically to generate CT Images to give an image appearance, as shown on axial NPS plots, similar to traditional FBP images while maintaining the performance of ASiR-V in the following areas: image noise (pixel standard deviation), low contrast detectability, high-contrast spatial resolution, and streak artifact suppression.

The images produced are branded as "TrueFidelity™ CT Images". Reconstruction times with Deep Learning Image Reconstruction software support a normal throughput for routine CT.

The deep learning technology is integrated into the scanner's existing raw data-based image reconstruction chain to produce DICOM compatible "TrueFidelity "" CT Images".

The system allows user selection of three strengths of Deep Learning Image Recon: Low, Medium or High. The strength selection will vary with individual users' preferences and experience for the specific clinical need.

Deep Learning Image Reconstruction software was initially introduced on the Revolution CT systems (K133705, K163213). The DLR algorithm is now ported to Revolution EVO (K131576), which offers 64 detector row and up to 40mm collimation, and ASIR-V reconstruction option.

Here's a breakdown of the acceptance criteria and study details based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document doesn't explicitly state quantitative "acceptance criteria" in a pass/fail format with numerical thresholds. Instead, it describes performance goals relative to the predicate device (ASiR-V) or traditional FBP images. The reported device performance generally indicates "as good as or better than" the reference.

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

• Sample Size: 60 retrospectively collected clinical cases.
• Data Provenance: Retrospective. The origin country is not explicitly stated, but the submitter is GE Healthcare Japan Corporation, so it's possible some or all cases originated from Japan or a region where GE Healthcare Japan Corporation operates.

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

• Number of Experts: 7 board-certified radiologists.
• Qualifications: Board-certified radiologists with expertise in the specialty areas that align with the anatomical region of each case. The document does not specify years of experience.

4. Adjudication Method for the Test Set

• Adjudication Method: Each image was read by 3 different radiologists who provided independent assessments of image quality. The readers were blinded to the results of other readers' assessments. There is no explicit mention of an adjudication process (e.g., 2+1 or 3+1 decision) for discrepant reader opinions; it appears the individual assessments were analyzed.

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

• MRMC Study: Yes, a clinical reader study was performed where 7 radiologists read images reconstructed with both ASiR-V (without DLIR) and DLIR.
• Effect Size of Human Reader Improvement: The document states that readers were asked to "compare directly the ASIR-V and Deep Learning Image Reconstruction (DLIR) images according to three key metrics of image quality preference – image noise texture, image sharpness, and image noise texture homogeneity." It reports that the results support substantial equivalence and performance claims and implies a preference for DLIR images, but does not quantify the effect size of how much human readers "improve" with AI assistance in terms of diagnostic accuracy or efficiency. The study primarily focused on radiologists' preference for image quality characteristics.

6. If a Standalone (i.e. algorithm only without human-in-the-loop performance) Was Done

• Standalone Performance: Yes, extensive non-clinical engineering bench testing was performed where DLIR and ASiR-V reconstructions were compared using identical raw datasets. This included objective metrics such as Low Contrast Detectability (LCD), Image Noise (pixel standard deviation), High-Contrast Spatial Resolution (MTF), Streak Artifact Suppression, Noise Power Spectrum (NPS), CT Number Accuracy and Uniformity, and Contrast to Noise (CNR) ratio. This constitutes a standalone (algorithm-only) performance evaluation.

7. The Type of Ground Truth Used

• For the Reader Study (Clinical Performance): The ground truth for evaluating diagnostic use was based on the assessment of image quality related to diagnostic use according to a 5-point Likert Scale by board-certified radiologists. This is a form of expert consensus on image quality suitable for diagnosis, rather than a definitive "truth" established by pathology or patient outcomes.
• For the Bench Testing (Technical Performance): The "ground truth" was the objective measurement of various image quality metrics (e.g., pixel standard deviation for noise, MTF for spatial resolution) in phantoms, which have known properties.

8. The Sample Size for the Training Set

• The document states that the Deep Neural Network (DNN) used in Deep Learning Image Reconstruction was "trained specifically" but does not disclose the sample size of the training set.

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

• The document implies that the DNN was trained to generate CT Images to give an image appearance similar to traditional FBP images while maintaining ASiR-V performance in certain areas. This suggests that existing "traditional FBP images" or images reconstructed with "ASiR-V" served as a reference or a form of "ground truth" for the training process. However, the exact methodology for establishing ground truth during the training phase (e.g., using paired low-dose/high-dose images, or simulated noise reduction) is not detailed in the provided text.

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The system is intended to produce cross-sectional images of the body by computer reconstruction of x-ray transmission data taken at different angles and planes, including Axial, Cine, Helical (Volumetric), Cardiac, and Gated acquisitions. 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 in patients of all ages.

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

The Revolution Maxima CT system is composed of a gantry, patient table, operator console, host computer, and power distribution unit (PDU), and interconnecting cables. The system also includes image acquisition and reconstruction hardware/software, general system software, accompanying documents, and associated accessories, interconnections. The Revolution Maxima system is an evolutionary configuration of the predicate Optima CT660 CT system (K131576). All of the hardware functionality is identical to the predicate, however, some hardware changes have been made that did not change the system's performance specifications such as the new liquid metal bearing tube for improved tube life and reliability, the upgraded DAS and detector to improve the manufacturability and lower electronic nosie for better low signal performace and thermal management.

Identical to the predicate, Revolution Maxima generates cross-sectional images of the body by computer reconstruction of x-ray transmission data taken at different angles and planes, including Axial, Cine, Helical (Volumetric), Cardiac, and Gated acquisitions modes. Revolution Maxima's Intended Use and Indications for Use remain identical to those of the predicate device.

The Revolution Maxima CT system is a head and whole body CT system incorporating the same basic fundamental operating principles and the same indications for use as the predicate device. It's materials and construction are identical to our existing marketed products. Revolution Maxima remains compliant with IEC 60601-1 Ed. 3.1 and associated collateral and particular standards, NEMA XR 25, XR 28, and 21 CFR Subchapter J performance standards. The accompanying documents also contain the information in support of IEC61223-3-5 Ed. 1.0 for acceptance testing.

The performance and image quality specifications are identical/equivalent to the predicate. The gantry has a 70 cm bore with a maximum FOV of 50 cm. Available rotation speeds range from 0.35 to 2.0 seconds. Same as the predicate, the Revolution Maxima has three types of reconstruction methods available: FBP, ASiR, and ASiR-V (K133640). The performance of ASiR-V on Revolution Maxima is identical to that on the predicate Optima CT660.

Revolution Maxima includes most of the available features on the current production predicate device Optima CT660. The new changes incorporated into Revolution Maxima are primarily addition of a few features found on other GE current production CT systems, e.g. updated newer ITE host computer, Digital Tilt as an alternative to the tilted gantry, improved metal artifacts reduction called Smart MAR and 1024x1024 Recon. Digital Tilt is a software reformatting of the reconstructed images so that they appear like those reconstructed from a tilted gantry. The smaller pixel sizes of 1024 Recon improves the spatial resolution which is useful in clinical applications that would benefit from increased spatial resolution, especially those applications where a large display field of view is desired to simultaneously image corresponding left/right anatomy for comparison (e.g. highresolution lungs, internal auditory canals).

There are other unique changes for Revolution Maxima which includes new gantry covers with more aesthetic and modern industry design, new gantry display panel on two tablets with high resolution touchscreens, one on each side of the gantry for improved access and usability. These gantry display tablets replace the single display screen located at the top center of the gantry in the predicate device and host the web based user interface for a new and improved workflow called "Express mode" which seamlessly displays the related protocols associated with order information in the RIS

This 510(k) summary for the Revolution Maxima CT system by GE Hangwei Medical Systems Co.,Ltd. describes its substantial equivalence to a predicate device, the Optima CT660. Therefore, the information provided is primarily focused on demonstrating that the new device is as safe and effective as the predicate, rather than establishing de novo acceptance criteria with standalone performance studies against a clinical ground truth.

Here's an analysis of the provided information, addressing your points where possible:

1. Table of Acceptance Criteria and Reported Device Performance

There isn't a table of specific acceptance criteria in the traditional sense of diagnostic performance metrics (e.g., sensitivity, specificity, AUC) for the Revolution Maxima. Instead, the "acceptance criteria" are implied by the claim of substantial equivalence to the predicate device, Optima CT660. The reported device performance is that it performs equivalently to the predicate.

• Non-clinical bench test results demonstrated the subject device performs equivalently to the predicate device.
• Image quality and dose performance confirmed using standard IQ and QA phantoms.
• Performance testing in accordance with IEC 61223-3-5 ed 2 (FDIS). |
  | Compliance with relevant standards and regulations | - In compliance with AAMI/ANSI ES 60601-1 and IEC60601-1 Ed. 3.1 and associated collateral and particular standards.
• Compliance to applicable CDRH 21 CFR subchapter J requirements.
• Compliance to NEMA standards XR 25, XR 26, and XR 28.
• Designed and manufactured under the Quality System Regulations of 21 CFR 820 and ISO 13485. |
  | Image quality specifications | Identical/equivalent to the predicate (Optima CT660), including ASiR and ASiR-V performance. |
  | Functional equivalence | - Operates on the same fundamental principles.
• Has the same intended use and indications for use.
• Hardware functionality is identical in some aspects, with new components not changing performance specifications (e.g., tube, DAS, detector for manufacturability and lower electronic noise).
• New features (1024x1024 Recon, Smart MAR, Digital Tilt) are additions, not changes to core functionality. |

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

The document explicitly states: "The Revolution Maxima can be fully tested on the engineering bench thus no additional clinical testing was required." This indicates that no human patient imaging test set was used for a clinical study to establish standalone diagnostic performance. Instead, testing relied on non-clinical bench tests, image quality and QA phantoms.

Therefore:

• Sample size for test set: Not applicable (no human patient test set).
• Data provenance: Not applicable (no human patient data). The data provenance refers to phantom and bench test data.

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

Since no clinical test set was used, there were no experts needed to establish ground truth for a clinical diagnostic performance evaluation. The "ground truth" for the non-clinical tests would be the known properties of the phantoms and the expected physical performance metrics, assessed by engineers and physicists rather than medical experts for diagnostic accuracy.

4. Adjudication Method for the Test Set

Not applicable, as no clinical test set requiring expert adjudication was used.

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 such MRMC study was performed or is mentioned. The device is a CT scanner, not an AI-assisted diagnostic tool that integrates with human readers in that manner. The "AI" mentioned (ASiR, ASiR-V) are iterative reconstruction algorithms incorporated into the image processing of the CT scanner itself to improve image quality and reduce dose, not a separate AI system intended for radiologists' interpretative assistance.

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

This refers to the performance of the CT system itself in generating images. The non-clinical testing served this purpose:

• Standalone Performance: Yes, standalone system performance was evaluated through non-clinical bench tests, image quality (IQ), and dose performance using standard IQ and QA phantoms. This includes specific tests like Metal Artifact Reduction (MAR) testing and image quality testing on 1024x1024 Recon, and performance testing in accordance with IEC 61223-3-5 ed 2 (FDIS). The results demonstrated equivalence to the predicate device.

7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc)

For the non-clinical tests, the "ground truth" was based on:

• Known phantom properties: Phantoms designed with specific, measurable physical characteristics (e.g., density, spatial resolution patterns).
• Physical laws and engineering specifications: The expected performance based on the design and validated against established physical principles and engineering parameters.
• Compliance with industry standards: Meeting the specified requirements of standards like IEC 61223-3-5, NEMA XR 25, 26, 28, etc.

8. The Sample Size for the Training Set

Not applicable. This is a 510(k) for a CT scanner, not a machine learning algorithm that requires a training set of images with ground truth labels. While the iterative reconstruction algorithms (ASiR, ASiR-V) might have involved data for their development, that information is not part of this 510(k) summary focused on the substantial equivalence of the overall CT system.

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

Not applicable, as there is no mention of a training set in this context.

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