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Single-Examination Risk Prediction of Severe Retinopathy of Prematurity
- Source :
- Pediatrics. 148(6)
- Publication Year :
- 2021
-
Abstract
- BACKGROUND AND OBJECTIVES Retinopathy of prematurity (ROP) is a leading cause of childhood blindness. Screening and treatment reduces this risk, but requires multiple examinations of infants, most of whom will not develop severe disease. Previous work has suggested that artificial intelligence may be able to detect incident severe disease (treatment-requiring retinopathy of prematurity [TR-ROP]) before clinical diagnosis. We aimed to build a risk model that combined artificial intelligence with clinical demographics to reduce the number of examinations without missing cases of TR-ROP. METHODS Infants undergoing routine ROP screening examinations (1579 total eyes, 190 with TR-ROP) were recruited from 8 North American study centers. A vascular severity score (VSS) was derived from retinal fundus images obtained at 32 to 33 weeks’ postmenstrual age. Seven ElasticNet logistic regression models were trained on all combinations of birth weight, gestational age, and VSS. The area under the precision-recall curve was used to identify the highest-performing model. RESULTS The gestational age + VSS model had the highest performance (mean ± SD area under the precision-recall curve: 0.35 ± 0.11). On 2 different test data sets (n = 444 and n = 132), sensitivity was 100% (positive predictive value: 28.1% and 22.6%) and specificity was 48.9% and 80.8% (negative predictive value: 100.0%). CONCLUSIONS Using a single examination, this model identified all infants who developed TR-ROP, on average, >1 month before diagnosis with moderate to high specificity. This approach could lead to earlier identification of incident severe ROP, reducing late diagnosis and treatment while simultaneously reducing the number of ROP examinations and unnecessary physiologic stress for low-risk infants.
- Subjects :
- Risk
Pediatrics
medicine.medical_specialty
Fundus Oculi
Birth weight
Gestational Age
Fundus (eye)
Logistic regression
Sensitivity and Specificity
Severity of Illness Index
Artificial Intelligence
Predictive Value of Tests
medicine
Birth Weight
Humans
Retinopathy of Prematurity
business.industry
Childhood blindness
Postmenstrual Age
Infant, Newborn
Gestational age
Retinopathy of prematurity
medicine.disease
Early Diagnosis
Logistic Models
Clinical diagnosis
Area Under Curve
Pediatrics, Perinatology and Child Health
business
Subjects
Details
- ISSN :
- 10984275
- Volume :
- 148
- Issue :
- 6
- Database :
- OpenAIRE
- Journal :
- Pediatrics
- Accession number :
- edsair.doi.dedup.....c649d10f16db51da31d3175507e67573