Johns Hopkins APL Explains Advantages of Deep Learning for Eye Health

Jeff Brody

Johns Hopkins University Applied Physics Laboratory has issued a new report describing how the medical community can apply deep learning to better diagnose eye problems.

In addition, APL said Thursday despite the growing number of research focused on using deep learning in the medical field, some challenges remain.

The report, “Deep learning in ophthalmology: The technical and clinical considerations,” details technical and practical factors that developers should consider when developing and deploying new algorithms.

“Deep learning algorithms can be used as alternative screening tools, especially as a way to rectify shortages in clinical staffing and expertise,” said Phil Burlina, an artificial intelligence and medical imaging expert at APL.

APL said design of the tools should prioritize training to analyze large image sets, coordination with retinal specialists and the capability to identify multiple eye conditions.

The United Nations estimates that 2B people will turn 60 years or older by 2050 across the world, which is expected to contribute to an increase in cases of visual impairment and blindness due to age-related conditions.

APL’s report also provides information on the areas where machine learning could help enhance treatment for eye problems.

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