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BU-CAD is a software application indicated to assist trained interpreting physicians in analyzing the breast ultrasound images of patients with soft tissue breast lesions suspicious for breast cancer who are being referred for further diagnostic ultrasound examination.

Output of the device includes regions of interest (ROIs) and lesion contours placed on breast ultrasound images assisting physicians to identify suspicious soft tissue lesions from up to two orthogonal views of a single lesion, and region-based analysis of lesion malignancy upon the physician's query. The region-based analysis indicates the score of lesion characteristics (SLC), and corresponding BI-RADS categories in user-selected ROIs or ROIs automatically identified by the software. In addition, BU-CAD also automatically classifies lesion shape, orientation, margin, echo pattern, and posterior features according to BI-RADS descriptors.

BU-CAD may also be used as an image viewer of multi-modality digital images, including ultrasound and mammography. The software includes tools that allow users to adjust, measure and document images, and output into a structured report (SR).

Patient management decisions should not be made solely on the basis of analysis by BU-CAD.

Limitations: BU-CAD is not to be used on sites of post-surgical excision, or images with Doppler, elastography, or other overlays present in them. BU-CAD is not intended for the primary interpretation of digital mammography images. BU-CAD is not intended for use on mobile devices.

BU-CAD developed by TaiHao Medical Inc. is a software system designed to assist users in analyzing breast ultrasound images including identification of regions suspicious for breast cancer and assessment of their malignancy. The system consists of a Viewer, a Lesion Identification Module, and a Lesion Analysis Module. The Viewer loads breast ultrasound and mammography images from local storage or PACS for review, and includes tools for measurement and image adjustment. The Lesion Identification Module identifies automated ROIs and generates lesion contours on breast ultrasound images. The Lesion Analysis Module analyzes given ROIs and generates a score of lesion characteristics (SLC), BI-RADS category, and BI-RADS descriptors. Users can replace automated ROIs with re-delineated rectangular ROIs for analysis. The last analysis results are displayed and modifiable by the user. BU-CAD also supports exporting CAD results to third-party reporting software.

The provided text describes the acceptance criteria and the study proving the device meets these criteria for the BU-CAD system.

Here's the breakdown of the information requested:

1. Table of Acceptance Criteria and Reported Device Performance

The document implies acceptance criteria by demonstrating performance gains in a comparative study against human readers. While explicit quantitative acceptance criteria for each metric are not stated, the success is determined by statistically significant improvement over unaided human performance and comparable performance to predicate devices. The primary acceptance criterion appears to be superiority of aided performance (AUC_LROC) over unaided performance.

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

• Test Set Sample Size:
  • MRMC Reader Study: 628 cases
  • Standalone Study: 1139 cases (which includes the 628 reader study cases plus 511 additional extended cases).
• Data Provenance:
  • MRMC Reader Study: 456 cases from the United States, 172 cases from Taiwan.
  • Standalone Study: 531 cases from North America, 36 cases from Europe, 572 cases from Taiwan.
• Retrospective or Prospective: The study is clearly stated as a retrospective study ("fully crossed multi-reader multi-case receiver operating characteristic (MRMC-ROC) retrospective study").

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

The document does not explicitly state the number of experts used to establish the ground truth for the test set. However, it mentions an "expert panel" in the context of defining ground truth ROIs for the robustness experiments.

Qualifications of Experts (Readers in MRMC study, likely similar for GT):

• 16 Readers participated in the MRMC study.
• Specialties: 14 Radiologists, 2 Breast Surgeons.
• Experience: Ranged from 1 year to >30 years of experience (as a radiologist/breast surgeon).
• Certifications/Training: Most radiologists (13/14) were MQSA certified. 4/14 radiologists had received Breast Image Fellowship training.

4. Adjudication Method for the Test Set

The document does not explicitly detail the adjudication method for establishing the definitive ground truth for the test set cases (e.g., how malignancy/benignity or BI-RADS categories were finalized if there were disagreements among initial assessments). However, it mentions "Pathology proof benign," "Two-year follow-up benign," and specific malignant pathology types (DCIS, IDC, ILC, Other cancer types) as the basis for benign/malignant case classification in the dataset demographics. This suggests pathology and clinical follow-up as the primary ground truth, not necessarily a reader adjudication process per se, for the malignancy outcome.

For the reader study itself, it was a "fully crossed" MRMC-ROC study where readers evaluate cases independently, both unaided and aided. The performance metrics (AUC, sensitivity, specificity, etc.) are derived from comparing individual reader's interpretations against the established ground truth.

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

• Yes, an MRMC comparative effectiveness study was done.
• Effect Size of Improvement:
  • Primary Objective (AUC_LROC Shift): The mean AUC_LROC shift was +0.0374. (Unaided AUC: 0.7786, Aided AUC: 0.8160). This improvement was statistically significant (p-value = 0.0001).
  • Comparison to Predicates: This shift (+0.0374) was stated to be "similar" to Koios DS for Breast (+0.037) and TransparaTM (+0.02).
  • Other Metrics (Aided vs Unaided):
    • Sensitivity: Increased from 0.9225 to 0.9353.
    • Specificity: Increased from 0.3165 to 0.3611.
    • NPV (unadjusted): Increased from 0.8623 to 0.8945.
    • PPV (unadjusted): Increased from 0.4876 to 0.5056.
    • Reduction in False Positives: A net benefit of +267 events (FP to TN) indicating reduction of false positives across all readers for benign cases.
    • Interpretation Time: Decreased by approximately 40%.

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

• Yes, a standalone study was done.
• Standalone Performance (AUC_LROC):
  • On the 628 reader study cases: 0.7987 (95% CI: 0.7626, 0.8348)
  • On the larger 1,139 standalone study cases: 0.8203 (95% CI: 0.7947, 0.8458)
• Standalone Sensitivity & Specificity (using BI-RADS 4A as cutoff):
  • Sensitivity: 88.33% (439/497)
  • Specificity: 57.94% (372/642)
• Lesion Identification Module (CADe) Accuracy: 93.24% (1062/1139) detected and localized correctly.

7. The Type of Ground Truth Used (Expert Consensus, Pathology, Outcomes Data, etc.)

The ground truth for the test set (both reader study and standalone study cases) was primarily established by:

• Pathology Proof: For malignant cases (Ductal carcinomas in situ (DCIS), invasive ductal carcinoma (IDC), Invasive lobular carcinoma (ILC), and other cancer types) and some benign cases.
• Two-year Follow-up: For other benign cases.

This indicates a strong, objective ground truth based on definitive clinical outcomes.

8. The Sample Size for the Training Set

The document does not provide the sample size for the training set. It only states that the "testing dataset was not used for training of BU-CAD algorithms."

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

The document does not provide information on how the ground truth for the training set was established. Since the test set ground truth was largely based on pathology and follow-up, it is highly probable that the training data followed similar rigorous ground truth establishment methods, but this is not explicitly stated.

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Koios DS is an artificial intelligence (Al)/machine learning (ML)-based computer-aided diagnosis (CADx) software device intended for use as an adjunct to diagnostic ultrasound examinations of lesions or nodules suspicious for breast or thyroid cancer.

Koios DS allows the user to select or confirm regions of interest (ROIs) within an image representing a single lesion or nodule to be analyzed. The software then automatically characterizes the selected image data to generate an AI/ML-derived cancer risk assessment and selects applicable lexicon-based descriptors designed to improve overall diagnostic accuracy as well as reduce interpreting physician variability.

Koios DS may also be used as an image viewer of multi-modality digital images, including ultrasound and mammography. The software includes tools that allow users to adjust, measure and document images, and output into a structured report.

Koios DS software is designed to assist trained interpreting physicians in analyzing the breast ultrasound images of adult (>= 22 years) female patients with soft tissue breast lesions and/or thyroid ultrasounds of all adult (>= 22 years) patients with thyroid nodules suspicious for cancer. When utilized by an interpreting physician who has completed the prescribed training, this device provides information that may be useful in recommending appropriate clinical management.

Limitations:

· Patient management decisions should not be made solely on the results of the Koios DS analysis.

· Koios DS software is not to be used for the evaluation of normal tissue, on sites of post-surgical excision, or images with doppler, elastography, or other overlays present in them.

· Koios DS software is not intended for use on portable handheld devices (e.g. smartphones or tablets) or as a primary diagnostic viewer of mammography images.

• The software does not predict the thyroid nodule margin descriptor, extra-thyroidal extension. In the event that this condition is present, the user may select this category manually from the margin descriptor list.

Koios DS is a software application designed to assist trained interpreting physicians in analyzing breast and thyroid ultrasound images. The software device is a web application that is deployed to a Microsoft IIS web server and accessed by a user through a compatible client. Once logged in and granted access to the Koios DS application, the user examines selected breast or thyroid ultrasound DICOM images. The user selects Regions of Interest (ROls) of orthogonal views of a breast lesion or thyroid nodule for processing by Koios DS. The ROI(s) are transmitted electronically to the Koios DS server for image processing and the results are returned to the user for review.

The Koios Medical, Inc. Koios DS device is an AI/ML-based computer-aided diagnosis (CADx) software that assists in the analysis of breast and thyroid ultrasound images.

Here's an overview of its acceptance criteria and the studies proving it meets them:

Acceptance Criteria and Reported Device Performance

Study Details:

2. Sample Sizes and Data Provenance:

• Test Set (Clinical Study):

  • Breast Functionality: 900 lesions from 900 different patients. (From predicate K190442, used for comparison). An additional 50 new cases were added to the breast set to test for robustness to dataset drift.
  • Thyroid Functionality: 650 retrospectively collected cases (lesions) from 650 different patients.
    • 500 cases from United States locations.
    • 150 cases from European locations.
  • Data Provenance: Retrospective for both breast and thyroid. Sourced from a wide variety of ultrasound hardware.

• Training Set:

  • Breast Engine: "A large database of known cases." (Specific number not provided in the summary, but the test set of 900 lesions was "set aside from the system's training data").
  • Thyroid Engine: "A large database of known cases." (Specific number not provided, but the test set of 500 lesions was "set aside from the system's training data"). The training data was separate from the independent site data used in bench testing.

3. Number of Experts and Qualifications for Ground Truth:

• The document implies that ground truth for the clinical studies relied on pathology/follow-up outcomes, meaning clinical experts (pathologists, clinicians) established the definitive diagnosis.
• For the reader studies (MRMC), the "readers" themselves were the experts whose performance was being evaluated.
  • Breast Study (K190442): 15 readers. Their qualifications varied:
    • Board Certification/Specialty: Diagnostic Radiology, Breast Surgeon, OB/GYN, Interventional Radiology.
    • Breast Fellowship Trained and/or Dedicated Breast Imager: 6 out of 15 had this.
    • Years of Experience (Mammography and/or Breast Ultrasound): Ranged from 0 years to 30 years.
    • Academic Institution Affiliation: Mixed (Yes/No).
    • MQSA Qualified Interpreting Physician: Mixed (Yes/No).
  • Thyroid Study (CRRS-3): 15 readers. Their qualifications varied:
    • Reader Category: Domestic Endocrinologist (End), Domestic Radiologist (Rad), European Rad, European End.
    • Experience (post-residency): Ranged from

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EchoGo Pro v1.0.2 is a machine learning-based decision support system, indicated as an adjunct to diagnotic stress echocardiography for patients undergoing assessment for coronary artery disease (CAD). When utilized by an interpreting physician, this device provides information that may be useful in rendering an accurate diagnosis. Patient management decisions should not be made solely on the results of the EchoGo Pro v 1.0.2 analysis. EchoGo Pro v 1.0.2 is to be used with stress echo exam protocols that contain A2C, A4C and mid-ventricular short-axis views at rest and at peak stress. EchoGo Pro v1.0.2 is not intended for the assessment of mild or moderate myocardial ischemia, or localization of coronary artery disease, or for the assessment of myocardial perfusion, myocardial viability or valve disease.

EchoGo Pro v1.0.2 is a standalone software application that utilizes anonymized DICOM 3.0 compliant stress echo (SE) datasets to provide a categorical assessment as to whether the data are suggestive of a higher or lower possibility of significant CAD. The software automatically registers images, and segments and analyses selected regions of interest (ROI). EchoGo Pro v1.0.2 utilizes standard clinical SE protocols that provide apical 2 chamber and parasternal short axis (SAX) views.

Ultrasound images are acquired from a third-party acquisition device. The incoming DICOM study is checked for consistency and completeness, i.e. whether all required views labels are present in metadata. Once the technical QC has been performed on the DICOM datasets, the algorithm for automated contour detection of the endocardium of the LV is applied and presented for review and approval by trained Operators. An auto-contouring algorithm places a trace around the LV that sufficiently captures the LV shape. Outlining is detected for all frames in between and including end-systole (ES) and end-diastole (ED) for AP2, AP4, and mid-ventricular short axis (SAX) views of the LV at both rest and peak stress. Trained operators review and approve of the contour traces. The approved contour traces are used in calculations for geometric parameters.

Geometric parameters are calculated from the approved contours and are fed into a fixed classification model that has been previously trained on datasets with known outcomes. The output of the pre-trained model generates a report which contains a categorical assessment as to whether the data are consistent with significant CAD or not. Significant CAD as determined by EchoGo Pro, based on LV segmentation (ROI) analysis, and was defined as ≥70% stenosis in the proximal to mid LAD, proximal left circumflex or proximal to mid RCA as measured by invasive angiography performed within 6 months of stress echocardiogram.

Here's an analysis of the acceptance criteria and study findings for the EchoGo Pro device based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

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

The document does not explicitly state the specific numerical sample size (number of patients/cases) for the test set used in the performance/clinical testing. It only mentions that "ROC curves were generated and analyzed" and refers to "readers" interpreting "ultrasound studies."

The data provenance (country of origin, retrospective/prospective) is not specified in the provided text.

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

The document does not explicitly state the number of experts used to establish the ground truth for the test set, nor does it specify their qualifications (e.g., "radiologist with 10 years of experience"). It mentions that ground truth was based on "known outcomes" for the training set and that "Significant CAD... was defined as ≥70% stenosis in the proximal to mid LAD, proximal left circumflex or proximal to mid RCA as measured by invasive angiography." This implies that the ground truth for CAD status was likely derived from invasive angiography reports, which are interpreted by cardiologists or interventional cardiologists.

4. Adjudication Method for the Test Set

The document does not explicitly state the adjudication method used for the test set. It mentions "known outcomes" and diagnostic performance with readers, but not how ground truth disagreements were resolved or if a consensus panel was used for the test set.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size of How Much Human Readers Improve with AI vs. Without AI Assistance

Yes, a multi-reader comparative effectiveness study was done. This is evident from the "primary endpoint of the study," which assessed "the difference between the diagnostic performance of readers when interpreting ultrasound studies with and without the assistance of EchoGo Pro v1.0.2."

The effect size of improvement for human readers with AI assistance (USE + EGP) vs. without AI assistance (USE Alone) in terms of AUROC was 0.054. This means that, on average, the area under the receiver operating characteristic curve for readers improved by 0.054 when using EchoGo Pro.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

Yes, a standalone performance evaluation was done. The document states: "The system achieved a native system performance of 0.927 AUROC, with specificity of 0.927 (95% CI 0.878, 0.976) and a sensitivity of 0.844 (95% CI 0.739, 0.950)." This "native system performance" refers to the algorithm's performance without direct human-in-the-loop diagnostic interpretation.

7. The Type of Ground Truth Used

The ground truth for "significant CAD" was defined as ≥70% stenosis in the proximal to mid LAD, proximal left circumflex, or proximal to mid RCA as measured by invasive angiography performed within 6 months of stress echocardiogram. This is a form of outcome data (angiographically confirmed disease status), considered a high-fidelity ground truth for CAD.

8. The Sample Size for the Training Set

The sample size for the training set is not specified in the provided text. It is mentioned that the "fixed classification model... has been previously trained on datasets with known outcomes," but no quantitative details about these datasets are given.

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

The ground truth for the training set was established based on known outcomes, specifically "Significant CAD as determined... based on LV segmentation (ROI) analysis, and was defined as ≥70% stenosis in the proximal to mid LAD, proximal left circumflex or proximal to mid RCA as measured by invasive angiography performed within 6 months of stress echocardiogram." This indicates that the training data also utilized invasive angiography results to confirm the CAD status, similar to the ground truth definition for the test set.

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

The LOGIQ E10 is a full featured, track 3, general purpose diagnostic ultrasound system which consists of a mobile console that provides digital acquisition, processing and display capability. The user interface includes a computer keyboard, specialized controls, LCD touch screen and color widescreen monitor. The system utilizes a variety of linear, curved, phased, dual, and matrix array transducers to support the broad imaging capabilities.

The provided FDA submission for the GE LOGIQ E10 does not contain any information regarding acceptance criteria or a study proving the device meets those criteria, especially in the context of an AI/ML component or performance metrics for diagnostic accuracy beyond general equivalence claims.

The document primarily focuses on establishing substantial equivalence of the LOGIQ E10 ultrasound system to predicate devices based on:

• Intended Use: Similar clinical applications.
• Technology: Same fundamental scientific technology (ultrasound imaging).
• Components: Similar transducers and system capabilities (measurements, digital imaging, reporting).
• Safety Standards: Compliance with electrical, thermal, electromagnetic safety, and biocompatibility.
• Software Features: Identical software features with some migrations from other GE systems (Voluson E10, LOGIQ E9, LOGIQ S8) and a new feature (UGAP) similar to one on another predicate.

The document explicitly states: "The subject of this premarket submission, LOGIQ E10, did not require clinical studies to support substantial equivalence." This means no specific performance metrics comparing the LOGIQ E10 to a gold standard or human readers were presented for this submission.

Therefore, I cannot fulfill the request for information regarding acceptance criteria, device performance, sample sizes, ground truth establishment, or any details about MRMC or standalone AI studies, as this information is not present in the provided text.

The information provided only demonstrates that the device is an ultrasound system with various imaging capabilities, and its submission for FDA clearance relies on substantial equivalence to existing predicate devices, rather than a de novo clinical performance study showcasing specific diagnostic accuracy metrics.

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The LOGIQ E10s is a general purpose diagnostic ultrasound system intended for use by qualified and trained healthcare professionals for ultrasound imaging, measurement, display and analysis of the human body and fluid. LOGIQ E10s clinical applications include : Fetal / Obstetrics; Abdominal (including Renal, Gynecology/Pelvic); Pediatric; Small Organ (Breast, Testes, Thyroid); Neonatal Cephalic; Cardiac (Adult and Pediatric); Peripheral Vascular; Musculo-skeletal Conventional and Superficial; Urology (including Prostate); Transvaginal; Transesophageal and Intraoperative (Vascular).

Modes of operation include: B, M, PW Doppler, CW 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/Color/PWD. B/Power/PWD. The LOG10 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 that provides digital acquisition, processing and display capability. The user interface includes a computer keyboard, specialized controls, high resolution color touch screen, and color widescreen monitor. The system utilizes a variety of linear, curved, phased and matrix array transducers to support the broad imaging capabilities.

The provided text states that the LOGIQ E10s did not require clinical studies to support substantial equivalence. Therefore, there is no information available in the document regarding acceptance criteria or a study proving the device meets acceptance criteria through clinical trials.

The document focuses on non-clinical tests and comparisons to predicate devices to establish substantial equivalence.

Here's a breakdown of the available information based on your requested points, highlighting the absence of clinical study data:

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

   • Not available. The document does not provide a table of acceptance criteria or reported device performance from a clinical study. It discusses non-clinical compliance with safety standards and similarity to predicate devices.

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

   • Not applicable. No clinical test set information is provided as clinical studies were not required.

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. No clinical test set information is provided.

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

   • Not applicable. No clinical test set information is provided.

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. No MRMC comparative effectiveness study was done or reported. This device is a diagnostic ultrasound system, not explicitly an AI-assisted diagnostic tool as described in the question.

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

   • Not applicable. This question pertains to AI algorithms. While the device connects to "Koios DS for Breast" (K190442), which is an AI-based system, the document refers to the LOGIQ E10s as a diagnostic ultrasound system, not an AI algorithm itself. No standalone performance of an algorithm is reported for the LOGIQ E10s.

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

   • Not applicable. No clinical test set information is provided.

8. The sample size for the training set

   • Not applicable. No clinical test information or AI training set information is provided for the LOGIQ E10s itself.

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

   • Not applicable. No clinical test information or AI training set information is provided.

Instead of clinical studies, the submission relies on documentation of compliance with safety standards and a comparison to predicate devices, stating: "The subject of this premarket submission, LOGIQ E10s, did not require clinical studies to support substantial equivalence." The conclusion is that the LOGIQ E10s is considered "as safe, as effective, and performance is substantially equivalent to the predicate device(s)."

The non-clinical tests performed included:

• Acoustic output
• Biocompatibility
• Cleaning and disinfection effectiveness
• Thermal, electrical, electromagnetic, and mechanical safety

The device was found to conform with applicable medical device safety standards, including:

• AAMI/ANSI ES60601-1
• IEC 60601-1-2
• IEC 60601-2-37
• ISO 10993-1
• ISO 14971
• NEMA PS 3.1-3.20 (DICOM Set)
• IEC 62359

Quality assurance measures applied during development included:

• Risk Analysis
• Requirements Reviews
• Design Reviews
• Testing on unit level (Module verification)
• Integration testing (System verification)
• Performance testing (Verification)
• Safety testing (Verification)

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