New Study

Combining Deep Learning Algorithm to AREDS Severity Scoring Improves AMD Tracking

by: Kevin Kunzmann

The binary judgment capabilities of deep learning (DL) algorithms may actually complement the common severity stratification scale used for age-related macular degeneration (AMD) diagnosis in informing a patient on their five-year disease outlook.

In a new study conducted by researchers at the Johns Hopkins University, investigators conducted analysis of 13-plus years of data from participants of the Age-Related Eye Disease (AREDS) four- and nine-step scoring for AMD eligibility criteria and severity scale scoring, respectively. They then attempted to develop automated methods that would implement the metrics of both AREDS testing criteria with DL algorithms to automatically evaluate severity of AMD and risk of progression to advanced AMD in patients based on fundus photographs.

Investigators hoped this combination of testing would alleviate physician burden of quickly and efficiently identifying individuals at different levels of risk of disease progression among the patient population.

The AREDS scoring scales, developed by the National Institutes of Health, differ in that the four-step scale is used in public domain to identify individuals with intermediate- or advanced-stage AMD who might be referred for more monitoring of its development; the nine-step scale is a refined combination of a drusen area scale and pigmentary abnormality scale.

“This detailed grading of fundus images can be time-consuming and likely limited to highly trained fundus photograph graders,” investigators wrote. “Given the complexity of the 9-step scale, in the absence of trained graders (eg, from fundus photograph reading centers), most physicians probably do not use the AREDS detailed AMD severity scale.”

The DL algorithms included in the analysis were deep convolutional neural networks (DCNNs), which use various computational layers that perform convolutions and nonlinear activation operations to identify image features that represent the original fundus image at various levels of abstraction.

Investigators used 3 DL-based methods to infer five-year AMD risk in individuals directly from the fundus image as input. These methods included soft prediction, hard prediction, and regressed prediction.

The study included 4613 participants of the AREDS data set, featuring 67,401 color fundus images……

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Source: MD Magazine