Abstract: 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.

Abstract: 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.

Abstract: Artificial intelligence (AI)-based guidance for an ultrasound device to improve capture of echo image views comprises receiving a plurality of the echo images captured by a probe of the ultrasound device, the ultrasound device including a display and a user interface (UI) that displays the echo images to a user. One or more neural networks process the echo images to continuously attempt to automatically classify the echo images by view type and generates corresponding classification confidence scores. The view type of the echo images are simultaneously displayed in the UI along with the echo images. Feedback indications are displayed in the UI of the ultrasound device to the user, where the feedback indications include which directions to move the probe of the ultrasound device so the probe can be placed in a correct position to capture and successfully classify the echo images.

Abstract: Artificial intelligence (AI) recognition of echocardiogram (echo) images by a mobile ultrasound device comprises receiving a plurality of the echo images captured by the ultrasound device, the ultrasound device including a display and a user interface (UI) that displays the echo images to a user, the echo images comprising 2D images and Doppler modality images of a heart. One or more neural networks process the echo images to automatically classify the echo images by view type. The view type of the echo images is simultaneously displayed in the UI of the ultrasound device along with the echo images. A report is generated showing the calculated measurements of features in the echo images. The report showing the calculated measurements is displayed on a display device.

Abstract: Artificial intelligence (AI) recognition of echocardiogram (echo) images by a mobile ultrasound device comprises receiving a plurality of the echo images captured by the ultrasound device, the ultrasound device including a display and a user interface (UI) that displays the echo images to a user, the echo images comprising 2D images and Doppler modality images of a heart. One or more neural networks process the echo images to automatically classify the echo images by view type. The view type of the echo images is simultaneously displayed in the UI of the ultrasound device along with the echo images. A report is generated showing the calculated measurements of features in the echo images. The report showing the calculated measurements is displayed on a display device.

Abstract: 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.

Abstract: 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.

Abstract: Artificial intelligence (AI)-based guidance for an ultrasound device to improve capture of echo image views comprises receiving a plurality of the echo images captured by a probe of the ultrasound device, the ultrasound device including a display and a user interface (UI) that displays the echo images to a user. One or more neural networks process the echo images to continuously attempt to automatically classify the echo images by view type and generates corresponding classification confidence scores. The view type of the echo images are simultaneously displayed in the UI along with the echo images. Feedback indications are displayed in the UI of the ultrasound device to the user, where the feedback indications include which directions to move the probe of the ultrasound device so the probe can be placed in a correct position to capture and successfully classify the echo images.

Abstract: Artificial intelligence (AI) recognition of echocardiogram (echo) images by a mobile ultrasound device comprises receiving a plurality of the echo images captured by the ultrasound device, the ultrasound device including a display and a user interface (UI) that displays the echo images to a user, the echo images comprising 2D images and Doppler modality images of a heart. One or more neural networks process the echo images to automatically classify the echo images by view type. The view type of the echo images is simultaneously displayed in the UI of the ultrasound device along with the echo images. A report is generated showing the calculated measurements of features in the echo images. The report showing the calculated measurements is displayed on a display device.