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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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EchoGo Core is intended to be used for quantification and reporting of cardiovascular function to support physician diagnosis. EchoGo Core is indicated for use in adult populations.

EchoGo Core is a standalone software application that automatically measures standard cardiac parameters including Ejection Fraction (EF), Global Longitudinal Strain (GLS), and Left Ventricular (LV) volume. EchoGo Core provides a service to calculate those values and provide a report back to the clinician. EchoGo Core analyzes echocardiogram cardiac parameters. An echocardiogram is performed using standard echocardiograms protocols. The anonymized echocardiogram dataset is transferred to Ultromics and once received the images are processed through the application's workflow. Once the technical QC has been performed, the automated contour detection of the endocardium of the LV is reviewed and approved by trained operators. Identifying and outlining the LV is standard practice in echocardiography and present in many other cleared devices, including the predicate device TomTec Arena TTA2 (K150122). In the proposed device, an auto-contouring algorithm places points around the LV that sufficiently capture the LV shape. These contours are used in calculations for geometric parameters. As with the TomTec Arena TTA2, the following parameters are calculated for inclusion in the report: EF: calculated from Simpson's Biplane Method (SBM) LV Volumes. GLSAVG: standard calculations, averaged from 2 chamber and 4 chamber views. LV volume: SBM calculated left ventricular volumes. A worksheet is automatically generated from the calculated parameters which is returned to the interpreting clinician. This report is intended as an additional input to standard diagnostic pathways and is only to be used by qualified clinicians.

Here's a breakdown of the acceptance criteria and study details for the EchoGo Core device, as extracted from the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

(Note: The exact numerical thresholds for "Below a set threshold" are not explicitly stated for all parameters, but the reported performance met these unquantified thresholds.)

2. Sample Size and Data Provenance for Test Set

• Sample Size: 378 previously acquired studies.
• Data Provenance: The text does not explicitly state the country of origin. It indicates the data was "previously acquired studies." The study was a "retrospective, non-interventional validation study."

3. Number of Experts and Qualifications for Ground Truth

The text states that the reference values, against which EchoGo Core was compared, were "generated using TomTec Arena TTA2 by a range of users (echocardiographers and cardiologists)." It also mentions that "EchoGo Core volume, ejection fraction and global longitudinal strain measurements were produced by a range of users (echocardiographers and cardiologists)."

• Number of Experts: "a range of users" (not a specific number provided).
• Qualifications of Experts: Echocardiographers and cardiologists.
• Note: The ground truth itself was not established by these experts directly, but rather by the predicate device (TomTec Arena TTA2) operated by these experts.

4. Adjudication Method for the Test Set

The text does not describe an explicit adjudication method for the test set. The primary comparison was between the EchoGo Core's output and the values generated by the predicate device (TomTec Arena TTA2). There's no mention of multiple experts independently evaluating and then adjudicating discrepancies for the ground truth establishment.

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

There is no mention of a formal MRMC comparative effectiveness study that assesses how much human readers improve with AI vs. without AI assistance. The study described focuses on the standalone performance of the EchoGo Core device compared to a predicate device.

However, the document does make a comparison related to user interaction with contours:

• "Inter and intra-operator variability (0% bias and RMSE) was eliminated" for EchoGo Core.
• "inter and intra-operator variability remained for the predicate, TomTec Arena TTA2 (5.3-12.0% and 2.5-4.7% RMSE for EF and GLS, respectively)."
• "manual contour editing was required in up to 100% of cases processed through the predicate, while contour rejection was required in up to 29% of cases processed through EchoGo Core."

This indicates an implicit benefit in eliminating operator variability, but it's not quantified in terms of improved human reader performance with AI assistance in a diagnostic task.

6. Standalone Performance Study

Yes, a standalone study was performed. The entire performance testing section describes the EchoGo Core device calculating parameters independently and then comparing these calculated values to those produced by the predicate device (TomTec Arena TTA2). The performance metrics (RMSE) directly refer to the algorithm's output.

7. Type of Ground Truth Used

The ground truth for the performance study was the measurements derived from the predicate device, TomTec Arena TTA2, operated by echocardiographers and cardiologists. This can be considered a form of "expert consensus/device-derived reference," where the extensively validated predicate device, used by experts, served as the reference standard.

8. Sample Size for the Training Set

The document explicitly states: "Test datasets were strictly segregated from algorithm training datasets." However, it does not provide the sample size for the training set.

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

The document states: "Test datasets were strictly segregated from algorithm training datasets." While it confirms segregation, it does not provide information on how the ground truth for the training set was established.

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