There are many similarities between this man’s project and mine. And if I had his knowledge I may have cracked my problem by now and would have new way to detect heart attacks early.
For intracranial pressure did you look at ocular ultrasound? How did that work out? Did you have ICPs from actual bolted patients for the gold standard / ground truth? That would be incredibly useful especially in patients on a ventilator who can’t provide a neuro exam.
Pneumothorax seems tricky since really it requires a lung point for diagnosis, and those can be hard to find. Did you simply look for lung sliding? That’s all a physician really needs to make an informed diagnosis.
I love hearing about this. Thank you for the work you do
It seems the primary way to detect regional wall motion abnormalities is with speckle tracking, which requires way too much post-processing for a clinician.
A system that segments the left ventricle and finds akinetic regions in realtime from a parasternal long axis view or an apical four chamber view would be pretty nifty.
If you know of a paper or system that does this now then please let me know. I would love for someone else to have solved this, haha.
My email is Davidm.Crockett [at] Utah.edu
Kudos for advancing the human race.
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.
The current datasets are just labeled anatomy at end systole and diastole.
Great questions, and you highlight the need for shared ultrasound data.