ROPing AI into Care
A promising application of artificial intelligence and deep learning in the search for plus (and pre-plus) disease in retinopathy of prematurity
Kang Zhang | | Quick Read
Landmark paper: JM Brown et al., “Automated Diagnosis of Plus Disease in Retinopathy of Prematurity Using Deep Convolutional Neural Networks”, JAMA Ophthalmol, 136, 803-810 (2018). PMID: 29801159.
Retinopathy of prematurity (ROP), a retinal vasoproliferative disease affecting premature infants, is a leading cause of childhood blindness worldwide. Standard clinical criteria have been established for diagnosis and treatment, and severe ROP can be successfully treated – if it is diagnosed early. The Early Treatment for Retinopathy of Prematurity multicenter clinical trial showed that “plus disease” is the most important parameter for identifying severe treatment-requiring ROP. Plus disease is defined as arterial tortuosity and venous dilation in the posterior pole, and accurate and consistent diagnosis of plus disease is critical to ensure that infants at risk of blindness receive the appropriate treatment. An intermediate stage – the pre-plus category – is defined as retinal vascular abnormalities that are insufficient for plus disease, but have more arterial tortuosity and venous dilation than normal.
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