In this post I am going to briefly review what has already been gleaned from 2 classic medical images—the retina and the electrocardiogram (ECG)—as representative for the exciting capability of machine vision to “see” well beyond human limits. Obviously, machines aren’t really seeing or interpreting and don’t have eyes in the human sense, but they sure can be trained from hundreds of thousand (or millions) of images to come up with outputs that are extraordinary.
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Of course, AI models have been shown to be quite useful for detecting eye diseases, such as diabetic retinopathy. But this is about the indirects, the not so obvious. That work has now extended to detection of kidney disease, control of blood glucose and blood pressure, hepatobiliary disease, a previous study on predicting heart attack, close correlation of the retinal vessels with the heart (coronary) artery calcium score, and, prior to the new report above, the ongoing prospective assessment and tracking of Alzheimer’s disease (“AlzEye,” Moorfields Eye Institute, UK, led by Professor Pearse Keane).
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Let’s turn to the ECG. As a cardiologist I’ve been reading these for over 35 years and I enjoy that, quickly recognizing patterns like rhythm abnormalities, enlargement of the heart, low voltage, or pericarditis. But I am no match for machine eyes. Here are the first crop of deep learning ECG reports which surprised me. I could never estimate a person’s age or sex, or their hemoglobin, by looking at an ECG, and it would be challenging to come up with an accurate assessment of a person’s ejection fraction, the main metric used for heart pump function.
But there’s more. Recently deep neural networks of ECGs have been trained to pick up valve disease, diabetes, predict atrial fibrillation that has risk of stroke, and pretty accurately predict the filling pressure (pulmonary capillary wedge) of the left ventricle (the heart’s main pumping chamber).
For the retina and the ECG, none of these machine vision capabilities have been put into practice with one major exception—the ejection fraction. Mayo Clinic did a randomized trial with primary care physicians, giving half the enhanced machine reading and the other group, with only standard machine reads, serving as controls. The accuracy of detecting patients with low ejection fraction was improved and this health system now provides these deep learning outputs routinely. Raising awareness for difficult diagnoses such as amyloid or hypertrophic cardiomyopathy is also getting incorporated in the leading edge Mayo ECG interpretations.
From the blog post:
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