Teaching the Model

At the outset, the team was aided by ophthalmologists at Aravind and Sankara Nethralaya to label the retina images. After a few short months, the model was trained to identify key markers of diabetic retinopathy, such as nerve tissue damage, swelling and hemorrhaging. And with a larger data set, Gulshan was sure that they could make the model even more accurate.

Enter Dr. Jorge Cuadros, head of the Eye Picture Archive Communication System (EyePACS), a telemedicine network connecting patients in rural areas across the United States to opthamologists for diabetic retinopathy scans. But patients seen by EyePACS still have to wait weeks for a graded scan, and Dr. Cuadros was happy to help any effort for a faster diagnosis.

The data EyePACS shared comprised a wide range of patients and was a hundred times as much as the AI team had gathered by that point. That meant a huge labelling workload because each image had to be graded multiple times to compensate for the bias of different graders. ‘The model learns what things they always did consistently’, says Dale Webster, a software engineer at Google. ‘This tends to result in something that’s a bit less biased and a bit more robust.’

To date, close to 100 ophthalmologists have rendered more than 1 million grades for the AI model.

Peng had a vision that the ARDA could be used in a clinical setting – but getting to that point required trials and regulatory approval. To do this, the team focused on two goals: conducting a clinical trial to begin testing the ARDA in the real world, and writing a paper about the results for the Journal of the American Medical Association (JAMA).

‘We wanted to go to JAMA because JAMA is about the practice of medicine’, says Nelson. ‘We didn’t just want to show that we could do this. We wanted to get on the map with doctors.’

Another part of getting the ARDA device on the map was presenting their work to the Food and Drug Administration (FDA). With Nelson at her side, Peng gave a ‘virtuoso performance’ on the virtues of AI. Peng was a key advocate and translator between the different communities involved in bringing the ARDA to life.

‘She can speak all languages’, Gulshan says, ‘so she could talk to us and understand the technical complexities of what we were doing, and also what the doctors were speaking, and what is relevant in terms of impact. Lily brought that and made it into something that we can now think of putting into a clinic.’

In fact, Dr. Raman imagines a device that’s as common as a thermometer or even a glucometer, a diagnostic tool that diabetics already use to monitor their blood sugar. ‘My job is not to screen for diabetic retinopathy’, he says. ‘My job is to do lasers, to do injections, to give – to really do surgeries and help them alleviate their blindness.’

For Dr. Cuadros, the key benefit of the ARDA is simple maths. He notes that the percentage of people with diabetic retinopathy in the United States is going down, indicating that preventative treatment is working. But because the rate of diabetes is increasing, the overall number of diabetic retinopathy patients remains the same. The number of people who need screening is on the rise, while the demand for treatment expertise remains the same.

And ophthalmologists feel the pinch.

But no matter the vector of diagnosis, all agree that awareness is key to health. In fact, getting a diabetic retinopathy diagnosis can lead to better outcomes overall. ‘If you detect retinal disease at an early stage when they don’t need treatment’, Dr. Cuadros says, ‘it’s still an opportunity for the patient to understand that diabetes is beginning to affect their body. Hopefully that would motivate them to control their blood sugar better.’