New deep learning model helps automated screening for common eye disorders

A new deep learning (DL) model capable of identifying disease-related features from eye images has been unveiled by a group of researchers from Tohoku University. This “lightweight” DL model can be trained with a small number of images, even those with a high degree of noise, and is resource efficient, meaning it is deployable on mobile devices.

The details were published in the journal Scientific reports May 20, 2022.

With many societies aging and medical personnel limited, self-monitoring and remote disease screening based on the DL model are becoming increasingly common. Yet deep learning algorithms are usually task-specific and identify or detect general objects such as humans, animals, or traffic signs.

Disease identification, on the other hand, requires precise measurement of tumors, tissue volume, or other types of abnormalities. To do this, a model must examine separate images and mark boundaries in a process called segmentation. But accurate prediction requires more computing power, which makes them difficult to deploy on mobile devices.

“There is always a trade-off between accuracy, speed and computational resources when it comes to DL models,” says Toru Nakazawa, study co-author and professor in the department of ophthalmology at the Tohoku University. “Our developed model has better segmentation accuracy and model training reproducibility even with fewer parameters, which makes it efficient and lighter compared to other commercial software.”

Professor Nakazawa, Associate Professor Parmanand Sharma, Dr. Takahiro Ninomiya and students from the Department of Ophthalmology worked with Professor Takayuki Okatani of Tohoku University’s Graduate School of Information Sciences to produce the model.

Using low-resource devices, they obtained measurements of the foveal avascular zone, an area with the fovea centralis in the center of the retina, to improve detection of glaucoma.

“Our model is also able to detect/segment optic discs and hemorrhages in fundus images with high accuracy,” Nakazawa added.

In the future, the group hopes to deploy the lightweight model to screen for other common eye disorders and other diseases.

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Materials provided by Tohoku University. Note: Content may be edited for style and length.

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