Clinical workflow to diagnose heart disease based on cardiac biomarker measurements and AI recognition of 2D and doppler modality echocardiogram images

A system, comprising:

• a memory storing a patient study comprising:
• i) one or more cardiac biomarker measurements derived from a patient sample, and ii) a plurality of echocardiogram (echo) images taken by an ultrasound device of a patient heart;
• a processor coupled to the memory;
• and
• a workflow engine executed by the processor that is configured to:
• separate, by a filter, the plurality of echocardiogram images according to 2D images and Doppler modality images based on analyzing image metadata;
• classify the 2D images by view type;
• classify the Doppler modality images by view type;
• segment regions of interest in the 2D images to produce segmented 2D images;
• segment the Doppler modality images to generate waveform traces to produce segmented Doppler modality images;
• use both the segmented 2D images and the segmented Doppler modality images to calculate measurements of cardiac features for both left and right sides of the heart;
• compare the one or more cardiac biomarker measurements and the calculated measurements of cardiac features with International cardiac guidelines;
• and
• output a report showing the one or more cardiac biomarker measurements and the calculated measurements that fall within or outside of the International guidelines.

A non-transitory computer-readable medium containing program instructions for implementing an automated workflow, which when executed by a processor configure the processor for:

• receiving, from a memory, a patient study comprising:
• i) one or more cardiac biomarker measurements derived from a patient sample, and ii) a plurality of echocardiogram images taken by an ultrasound device of a patient heart;
• separating, by a filter, the plurality of echocardiogram images according to 2D images and Doppler modality images based on analyzing image metadata;
• classifying the 2D images by view type;
• classifying the Doppler modality images by view type;
• segmenting cardiac chambers in the 2D images and segmenting the Doppler modality images to generate waveform traces, thereby producing segmented 2D images and segmented Doppler modality images;
• using both the segmented 2D images and the segmented Doppler modality images to calculate measurements of cardiac features for both left and right sides of the heart;
• comparing the one or more cardiac biomarker measurements and the calculated measurements of cardiac features with International cardiac guidelines;
• and
• outputting a report showing the one or more cardiac biomarker measurements and the calculated measurements that fall within or outside of the International guidelines.

A system, comprising:

• a server comprising a memory and a processor coupled to the memory, the server in network communication with remote computers at respective laboratories, the respective laboratories maintaining cardiac and echocardiogram (echo) biomarker files comprising one or more cardiac biomarker measurements and echocardiogram image files;
• and
• a workflow engine executed on the server that is configured to:
• download and install a client and a set of neural networks to a first remote computer of a first laboratory, the client accessing the cardiac and echo biomarker files of the first laboratory to train the set of neural networks and to upload a first trained set of neural networks to the server;
• download and install the client and the first trained set of neural networks to a second remote computer of a second laboratory, the client accessing the cardiac and echo biomarker files of the second laboratory to continue to train the first trained set of neural networks and to upload a second trained set of neural networks to the server;
• and
• continuing the process until the client and the second trained set of neural networks is downloaded and installed to a last remote computer of a last laboratory, the client accessing the cardiac and echo biomarker files of the last laboratory to continue to train the second trained set of neural networks and to upload a final trained set of neural networks to the server.