Abstract / Synopsis:
Technology allows ophthalmologists to determine severitiy of disease.
This article was reviewed by Siamak Yousefi, PhD
Artificial intelligence (AI)-enabled radar may be an improvement over the traditional method of visual field analysis. Investigators note that by using AI-enabled radar they can better assess the functionality of patients with glaucoma and determine which patients have slowly and rapidly progressing visual field damage.
This technology is designed to address the shortcomings of traditional visual field analysis, specifically, i.e., that they rely on traditional paradigms such as linear regression and do not generate detailed results beyond progression or no progression, do not provide objective identification of progression, and lack advanced visualization and interpretation, according to Siamak Yousefi, PhD.
“We have proposed a glaucoma radar, a dashboard, that is a pipeline of linear and non-linear data transformations and unsupervised machine learning that provides advanced visualization with three layers of glaucoma knowledge—including the global visual functional severity, extent of visual functional loss in the hemifields, and local patterns of visual field loss,” explained Dr. Yousefi, assistant professor, Departments of Ophthalmology and Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis.
“The system also provides personalized monitoring, data from thousands of previous glaucoma patients, and can identify rapid or slow progression,” he added.
Though the basis for this advanced-visualization technology is highly complex, the investigators sought to develop a screen for monitoring glaucoma that is user-friendly and able to be understood by non-medical individuals. It includes more than 13,000 visual fields cross-sectionally.
“We applied principle component analysis to linearly reduce the number of dimensions and extracted the global characteristics of the visual fields,” Dr. Yousefi said. “We then applied manifold learning to grab the local patterns of visual field loss and eventually generated this to a map.”
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To make sense of the data from the collected visual fields, the investigators first identified very dense areas, and then applied unsupervised clustering to identify 32 non-overlapping clusters that represented different levels of visual field severity.
According to Dr. Yousefi, when the mean deviation of each cluster was computed, the observation was that the severities of the visual fields increased moving from the top right to the bottom left of the screen.
“The AI pipeline was able to identify the global functional severity of the eyes in the various clusters, i.e., mostly normal eyes on the right and those with severe visual field loss on the left and bottom left of the screen,” he said.
The researchers also computed the glaucomatous severity in the inferior and superior hemifields.
“We noticed that the AI wisely put eyes with similar hemifield characteristics into different clusters, although statistically their global severity may have been similar,” noted Dr. Yousefi, indicating that the AI was able to differentiate similar damage in different locations and cluster those eyes together.