The company is saying you don't need a specialist, but after bayes theorem (using 90%TN 87%TP and D(A)= 200,000 complication / 29,100,000 diabetes), the chance you have this condition after the machine says you do is 0.83%.
https://www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/u...
If the images are of sufficient quality, the software provides the doctor with one of two results: (1) “more than mild diabetic retinopathy detected: refer to an eye care professional” or (2) “negative for more than mild diabetic retinopathy; rescreen in 12 months.” If a positive result is detected, patients should see an eye care provider for further diagnostic evaluation and possible treatment as soon as possible.
Are you interpreting the Verge or talking about some other statement?
> IDx-DR founder Michael Abràmoff told Science News. “It makes the clinical decision on its own.”
But I would guess a specialist would still need to be involved since it's not a fool-proof system. A specialist might take other symptoms or variables into account when making the diagnosis or order further tests. While this tool might be useful for blanket screening considering that it is harmless, it seems like it's hardly going to be "making the decision on its own" and prescribing treatment.
It's not something primary care doctors currently do.
What if you have the condition, the GP runs the test, and it turns out negative, and then the GP decides to not send you to a specialist ...
Can anybody infer what the confusion matrix looks like from reading the text?
90% of people are accurately detected as not having the disease, i.e. 10% FP rate. So ~3m people would be falsely diagnosed as having the complication, to 200k/3m have the disease.
10% isn't a great number, but it isn't clear from this coverage whether this complication is generally asymptomatic or not. If there are symptoms to go with it, the numbers may be far better.
I'm more worried about the 87% accurately detected as having the disease, i.e. 13% of false negatives (FN). I don't know how many general practitioners would actually send a patient to a specialist if the device did not detect changes.
The retina seems well suited to AI approaches, though, so I'd be interested in what comes next from companies like this, DeepMind, and other researchers/organizations (look out for Lee et al over at the University of Washington)
The standard practice today is that if a patient is determined to be diabetic then they get referred to an ophthalmologist visit once a year. In that case would comparing those rates of diagnosis be useful?
Not true. The aim is to treat patients before they become symptomatic. Outcomes are much worse otherwise.
2min promo on Diabetic Eye Screening: https://www.youtube.com/watch?v=PK1Y-1BKFn0
The false positive bayesian math is a good illustrative example, but reality is more complicated. And no doctor will base their diagnostic solely on one number.
P(H) = .0069
P(D|H) = .87
P(D|H')= .1 (10% chance the test says you have it but you don't)
P(H|D) = 0.057 or about 6% probability you have complication after machine says you do
I guess they may be misreporting sensitivity as accuracy.