AI may help spot newborns at risk for most severe form of bl
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An artificial intelligence (AI) device that has been fast-tracked for approval by the FDA 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.Babies born prematurely are at risk for retinopathy. That is, they have fragile vessels in their eyes, which can leak blood and grow abnormally. If left untreated, vessel growth can worsen and cause scarring, which can pull on and cause detachment of the retina, the light-sensing tissue at the back of the eye. Retinal detachment is the main cause of vision loss from ROP. Each year, the incidence of ROP in the United States is approximately 0.17%. Most cases are mild and resolve without treatment.

Upon birth, the eyes of preemies are screened and closely watched for signs of retinopathy. But ROP-related changes occur along a spectrum of severity. AP-ROP can elude diagnosis because its features can be more subtle and harder to appreciate than typical ROP. .

In the current study, nine neonatal care centers used deep learning to determine how well it detected AP-ROP. The 947 newborns in the study were followed over time and fundus images from a total of 5945 eye examinations were analyzed both by the deep learning system and a team of expert fundus image graders.Among all eyes followed, 3% developed AP-ROP. There was a significant level of inter-reader disagreement among the expert graders, suggesting the need for objective metrics of disease severity.

Importantly, 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 and younger .

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.

Infants with AP-ROP also were more likely to have comorbidities such as chronic lung disease, compared to infants without AP-ROP. The requirement for higher oxygen concentrations among infants with lung disease may have played a role in their eye disease, said Campbell. Decades ago, researchers made a connection between the routine use of high concentrations of oxygen at birth and an increase in the development of retinopathy. Oxygen is nearly always required for survival, but is titrated very carefully to maximize survival while minimizing the risk to vision.

The deep learning system in the clinical trial, the i-ROP DL system, was recently granted breakthrough status by the FDA, which accelerates its development and FDA review. Development of the device was supported by the NEI, part of the National Institutes of Health.