The Pitter-Patter of Tiny Diagnoses
A simple yet accurate model could help streamline ROP screening
Every year, retinopathy of prematurity (ROP) affects between 400 and 600 infants in the US (1). ROP continues to be a leading cause of strabismus, amblyopia, and severe near-sightedness in premature babies – and can lead to total vision loss, if not diagnosed and treated quickly. And therein lies the problem: the current model has low specificity for predicting which premature infants are most at risk of severe ROP; only 5–10 percent of those selected for a screening examination go on to receive treatment. But that looks set to change – thanks to a new model that boosts accuracy while maintaining, or even improving upon, sensitivity (1).
“Prior approaches were successful but limited by development studies that were too small, resulting in overfitted models and relatively complex calculations,” says Gil Binenbaum, who led the study. “But despite these limitations, we suspected we could combine successful ideas from each group of investigators into a more effective approach.” And thus, a hybrid model was born.
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