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AI detects eye disease and risk of Parkinson’s from retinal images

Scientists have developed an artificial intelligence (AI) tool capable of diagnosing and predicting the risk of developing multiple health conditions — from ocular diseases to heart failure to Parkinson’s disease — all on the basis of people’s retinal images.

AI tools have been trained to detect disease using retinal images before, but what makes the new tool — called RETFound — special is that it was developed using a method known as self-supervised learning. That means that the researchers did not have to analyse each of the 1.6 million retinal images used for training and label them as ‘normal’ or ‘not normal’, for instance. Such procedures are time-consuming and expensive, and are needed during the development of most standard machine-learning models.

Instead, the scientists used a method similar to the one used to train large-language models such as ChatGPT. That AI tool harnesses myriad examples of human-generated text to learn how to predict the next word in a sentence from the context of the preceding words. In the same kind of way, RETFound uses a multitude of retinal photos to learn how to predict what missing portions of images should look like.

“Over the course of millions of images, the model somehow learns what a retina looks like and what all the features of a retina are,” says Pearse Keane, an ophthalmologist at Moorfields Eye Hospital NHS Foundation Trust in London who co-authored a paper published today in Nature1 describing the tool. This forms the cornerstone of the model, and classifies it as what some call a foundation model, which means that it can be adapted for many tasks.

A window into human health

A person’s retinas can offer a window into their health, because they are the only part of the human body through which the capillary network, made up of the smallest blood vessels, can be observed directly. “If you have some systemic cardiovascular disease, like hypertension, which is affecting potentially every blood vessel in your body, we can directly visualize [that] in retinal images,” Keane says.

Retinas are also an extension of the central nervous system, sharing similarities with the brain, which means that retinal images can be used to evaluate neural tissue. “The rub is that a lot of the time people don’t have the expertise to interpret these scans. This is where AI comes in,” Keane says.

Once they had pre-trained RETFound on those 1.6 million unlabelled retinal images, Keane and his colleagues could then introduce a small number of labelled images say, 100 retinal images from people who had developed Parkinson’s and 100 from people who had not to teach the model about specific conditions. Having learnt from all the unlabelled images what a retina should look like, Keane says, the model is able to easily learn the retinal features associated with a disease.

Using unlabelled data to initially train the model “unblocks a major bottleneck for researchers”, says Xiaoxuan Liu, a clinical researcher who studies responsible innovation in AI at the University of Birmingham, UK. Radiologist Curtis Langlotz, director of the Center for Artificial Intelligence in Medicine and Imaging at Stanford University, in California, agrees. “High-quality labels for medical data are extremely expensive, so label efficiency has become the coin of the realm,” he says.

The system performed well at detecting ocular diseases such as diabetic retinopathy. On a scale where 0.5 represents a model that performs no better than a random prediction and 1 represents a perfect model that makes an accurate prediction each time, it scored between 0.822 and 0.943 for diabetic retinopathy, depending on the data set used. When predicting the risk for systemic diseases — such as heart attacks, heart failure, stroke and Parkinson’s — the overall performance was limited, but still superior to that of other AI models.

RETFound is so far one of the few successful applications of a foundation model to medical imaging, Liu says.

Expanding application

Researchers are now looking ahead to what other types of medical imaging the techniques used to develop RETFound might be applied to. “It will be interesting to see whether these methods generalize to more complex images,” Langlotz says — for example, to magnetic resonance images or computed tomography scans, which are often three- or even four-dimensional.

The authors have made the model publicly available, and hope that groups around the world will be able to adapt and train it to work for their own patient populations and medical settings. “They could potentially take this algorithm and fine-tune it, using data from their own country to have something that’s more optimized for their use,” Keane says.

“This is tremendously exciting,” Liu says. But using RETFound as the basis for other models to detect diseases comes with a risk, she adds. That’s because any limitations embedded in the tool could leak into future models that are built from it. “It is now up to the authors of RETFound to ensure its ethical and safe usage, including transparent communication of its limitations, so that it can be a true community asset.”


https://www.nature.com/articles/d41586-023-02881-2

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