Eko.ai Pte. Ltd.

Yoran Hummel

Su Ping Carolyn Lam

James Otis Hare, Ii

Mathias Iversen

Matthew Frost

Zhubo Jiang

Weile Wayne Tee

Cyril Equilbec

Sze Chi Lim

A computer-implemented method for automated diagnosis of cardiac amyloidosis (CA) and hypertrophic cardiomyopathy (HCM) performed by an automated workflow engine executed by at least one processor includes separating a plurality of echocardiogram (echo) images a heart according to 2D images and Doppler modality images. The 2D images are classified by view type, including A4C video. The 2D images are segmented to produce segmented A4C images having a segmentation mask over the left ventricle. Phase detection is performed on the segmented A4C images to determine systole and diastole endpoints per cardiac cycle. Disease classification is performed on beat-to-beat A4C images for respective cardiac cycles. The cardiac cycle probability scores generated for all of the cardiac cycles are aggregated for each A4C video, and the aggregated probability scores for all the A4C videos are combined to generate a patient-level conclusion for CA and HCM.

Automatic clinical workflow that recognizes and analyzes 2D and doppler modality echocardiogram images for automated cardiac measurements and diagnosis of cardiac amyloidosis and hypertrophic cardiomyopathy

A computer-implemented method for grading of Aortic Stenosis severity performed by an automated workflow engine executed by at least one processor includes receiving, from a memory, a plurality of echocardiogram images a heart. The plurality of echocardiogram (echo) images according to 2D images and Doppler modality images. The 2D images are classified by view type, and the Doppler modality images are classified by region. The regions of interest in the 2D images are segmented to produce segmented 2D images. The Doppler modality images are segmented to generate waveform traces to produce segmented Doppler modality images. The segmented images are used to calculate measurements of cardiac features of the heart. A conclusion of Aortic Stenosis severity is generated by comparing the calculated measurements to cardiac guidelines. A report is then output showing the calculated measurements of the cardiac features that fall within or outside of the cardiac guidelines.

Automatic clinical workflow that recognizes and analyzes 2D and doppler modality echocardiogram images for automated cardiac measurements and grading of aortic stenosis severity

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Singapore, Singapore

Yoran Hummel