Hi Jeremy. Thanks for your note. I’m certainly glad to be able to focus more on study.
The cancer was mentioned only in passing, but I’m inspired to say more as it relates to machine learning. Of course, what I had already learned in part of v1 was in the back of my mind during this adventure into the world of medicine and the medical system. The diagnosis and treatment of prostate cancer relies on many standard tests, each of which has distressingly low specificity and low sensitivity. Urologists then use tables, nomograms, and their own experience (and frankly their biases) to make a prognosis and treatment recommendation. And those assessments are all over the map, leaving the patient to play the odds as best he can.
I wondered if there is a way to combine these tests into a measure of higher accuracy. Such would be a great benefit to patients and doctors. Problems: the field is hampered by a lack of outcome measures (and agreement of what those should be), and a lack of “big data”. To be medically legitimate/publishable, any learned function would also have to provide not just a prediction but also a confidence interval, and be able to “justify” its reasoning. Those outputs are not something that ANNs typically provide. Of course, I don’t know much yet about machine learning, but intend to stay awake to any possible applications.
Second, I sat down with my radiologist (rumored to be one of the best in the US), and watched him rapidly and simultaneously look at transverse image slices in four signal channels. Then, with more deliberation, evaluate the level of malignancy and the certainty of his assessment. How does he achieve his high accuracy? “I’ve looked at tens of thousands of scans.” I thought, there must be a better way than looking at four 2D slices, one that would free up an expert’s diagnostic acumen from some of the mechanics of visualization. As a patient, honestly I would be reluctant to ultimately trust a computer’s assessment over that of an acknowledged expert. However, I think tools that automatically discover and visually augment important features could make an expert even better. Furthermore, tumors found on MRI are these days often biopsied and given a histological Gleason score. That provides some concrete data (though the histological scoring itself is highly subjective and variable). I can imagine someday a radiologist taking a “virtual biopsy” of a specific volume in the scan and getting back a predicted Gleason score with confidence interval. Such an assistant could prevent unnecessary biopsies and encourage necessary ones.
I’m just musing on these ideas at this point, and hope to better understand what is feasible as I work through the courses.