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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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