The i-ROP DL deep learning system quantified the dilation and tortuosity of the retinal vessels. (Credit: National Eye Institute)

An artificial intelligence (AI) device may help identify newborns at risk for aggressive posterior retinopathy of prematurity (AP-ROP). AP-ROP is the most severe form of ROP and can be difficult to diagnose in time to save vision.

A clearer, quantifiable AP-ROP patient profile emerged, which could help identify at-risk infants earlier. Infants who developed AP-ROP tended to be more premature. Compared with infants who needed treatment but never developed AP-ROP, AP-ROP infants were born lighter (617 vs. 679 g) and younger (24.3 weeks vs. 25.0 weeks). No infants born after 26 weeks developed AP-ROP in this population.

AP-ROP also tended to onset rapidly and quickly grow worse. Although rapid progression of disease has always been implied in the diagnosis of AP-ROP, to date there has been no way to measure this clinical feature. Monitoring the rate of vascular severity score changes could therefore improve detection of AP-ROP risk, according to the study findings.

The deep learning system in the clinical trial, the i-ROP DL system, was recently granted breakthrough status.

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This article first appeared in the May, 2020 issue of Medical Design Briefs Magazine.

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