[fpgaminer]

Correct me if I'm wrong (I haven't worked with health data science like this) ... wouldn't an end-to-end approach work better from the get-go?

What I mean is, rather than developing a segmentation algorithm and then a motion detection algorithm, why not just feed a bunch of frames into a CNN and have it directly predict "heart attack risk"?

Or is the segment-then-motion-detect approach necessary because of its better explainability?

I guess I view the end-to-end approach as being less fiddly than the more traditional computational imaging approach. And it has a bonus. If data is available, you could feed it historical ultrasound data from patients that later had heart attacks. With that, it's possible it will learn other features of an ultrasound that predict future heart attack.

[davycro]

That would require a dataset of ultrasounds from people having active myocardial infarctions, which we don’t have, and would take at least a year of academic coordination to assemble.

The current datasets are just labeled anatomy at end systole and diastole.

[fpgaminer]

Hmm, curious. I suppose I would wonder, then, if there's no dataset how any algorithm could be developed. What I mean is ... once you've developed something, how do you test how well it performs? How do you analyze the effectiveness of the algorithm, the false positive rate, etc.

[davycro]

I could assemble a small dataset of less than a hundred ultrasounds to test the algorithm. A big dataset that could train an AI would require quite the effort.

Great questions, and you highlight the need for shared ultrasound data.