Role of artificial intelligence in diagnostics increases

The advent of electronic medical records with large image databases, along with advances in artificial intelligence with deep learning, is offering medical professionals new opportunities to dramatically improve image analysis and disease diagnostics.

Photo
Examples of fundus images showing age-related macular degeneration. Top, left to right: disease-free and early stages of AMD. Bottom, left to right: intermediate and advanced stages of AMD.
Source: APL

Researchers from the Johns Hopkins University Applied Physics Laboratory (APL) in Laurel, Maryland, and collaborators at the Johns Hopkins School of Medicine have developed image analysis and machine learning tools to detect age-related macular degeneration (AMD). In Nature Medicine, members of the team discuss the potential of such tools to be used clinically and applied to other image-based medical diagnoses as well.

AMD causes lesions that blur the sharp, central vision individuals need for activities such as reading, recognizing faces and driving. It is the number-one cause of blindness for individuals over 50. Typically, much of the vision loss from AMD is irreversible, prompting the need to detect treatable lesions before substantial vision loss has occurred.

In 2015, APL’s Philippe Burlina and colleagues teamed up with the Johns Hopkins Wilmer Eye Institute on ways to automate AMD diagnosis. In recent work, they demonstrated that machine diagnostics using deep learning can match the performance of human ophthalmologists. “We’ve been able to show the feasibility of automated fine-grained classification of AMD severity that only highly trained ophthalmologists can achieve,” explained Burlina. “These techniques have the potential to provide individuals with automated grading of images to identify AMD or monitor those individuals with earlier stages of AMD for the onset of the more advanced stages when prompt treatment may be indicated to reduce the risk of blindness.”

The team has also expanded its inquiry to characterize retinal layers in optical coherence tomography, or OCT, which is a noninvasive imaging technique that provides high-resolution, cross-sectional images of the retina, retinal nerve fiber layer and optic nerve head. Such techniques can be used to diagnose other retinal diseases such as diabetic retinopathy but also have the potential to help characterize vascular and neurodegenerative pathologies. “We were able to show that machines can do as well as humans for diagnosing AMD,” Burlina said. “So now we have started looking at other retinal diseases, and how to combine images with other sources of information — demographics, lifestyle factors such as smoking, and sunlight exposure — to automatically perform prognosis and predict the probability for five-year risk of developing the advanced form of the disease. The end goal is to help clinicians and guide treatment.”

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