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AI IN REAL WORLD- Back in 2016, Google’s AI team have burdened themselves with tackling one of the fastest-growing illnesses of our time — diabetic eye diseases. Diabetic retinopathy (DR) — an eye condition, currently affects people with diabetes and is the fastest-growing cause of blindness, with nearly 415 million diabetic patients at risk worldwide.

Google’s researchers have developed a deep learning algorithm that can interpret signs of DR in retinal photographs, potentially helping doctors screen more patients, especially in communities where the resources are limited.

This deep learning algorithm showed great promise with results that are on par with ophthalmologists.

After three years of thorough testing and tweaking the model, a team of researchers have decided to put their model into practice. For this, they have chosen Thailand, where there are only about 1,400 eye doctors for approximately five million diabetics.

How Did It Go

Google AI, in partnership with the Ministry of Public Health in Thailand, conducted field research in clinics across the provinces of Pathum Thani and Chiang Mai for over a period of eight months.

During this period, the researchers made regular visits to 11 clinics, observed how the nurses of those clinics handled eye screenings and interviewed them to have a deeper understanding of the process. In the course of their trials, they found significant fundamental issues in the way the deep learning systems were deployed. Though the model was improved regularly, the challenges came from factors external to the model.

For instance, some images captured in screening might have issues like blurs or dark areas. An AI system might conservatively call some of these images “ungradable” because the issues might obscure critical anatomical features that are required to provide a definitive result. For clinicians, the gradability of an image may vary depending on one’s own clinical set-up or experience.

Two images of the same eye, with varied lighting

The system’s high standards for image quality is at odds with the consistency and quality of images that the nurses were routinely capturing under the constraints of the clinic.

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Article Credit: AIM