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.

中文翻译：

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严重早产儿视网膜病变的单次检查风险预测。

背景和目标 早产儿视网膜病变 (ROP) 是儿童失明的主要原因。筛查和治疗可降低这种风险，但需要对婴儿进行多次检查，其中大多数婴儿不会发展为严重疾病。先前的工作表明，人工智能可能能够在临床诊断之前检测到严重疾病（需要治疗的早产儿视网膜病变 [TR-ROP]）。我们的目标是建立一个将人工智能与临床人口统计学相结合的风险模型，以减少检查次数而不会遗漏 TR-ROP 病例。方法 从 8 个北美研究中心招募接受常规 ROP 筛查检查的婴儿（总共 1579 只眼睛，190 只眼睛使用 TR-ROP）。血管严重程度评分 (VSS) 来自于 32 至 33 周时获得的视网膜眼底图像 绝经后年龄。针对出生体重、胎龄和 VSS 的所有组合训练了七个 ElasticNet 逻辑回归模型。精确召回曲线下的面积用于识别性能最高的模型。结果 胎龄 + VSS 模型具有最高的性能（精度召回曲线下的平均值 ± SD 面积：0.35 ± 0.11）。在 2 个不同的测试数据集（n = 444 和 n = 132）中，敏感性为 100%（阳性预测值：28.1% 和 22.6%），特异性为 48.9% 和 80.8%（阴性预测值：100.0%）。结论 使用单一检查，该模型识别出所有患 TR-ROP 的婴儿，平均而言，在诊断前 > 1 个月具有中度到高度的特异性。这种方法可以导致更早地识别事件严重的 ROP，