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 learned from all the unlabelled images of 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.Q 8.   Which stylistic element is used in the passage to emphasize the importance of training RETFound with unlabelled data?a)  Alliterationb)  Hyperbolec)  Anaphorad)  Juxtaposition