Importance Retinopathy of prematurity (ROP) is a leading cause of preventable blindness that disproportionately affects children born in low- and middle-income countries (LMICs). In-person and telemedical screening examinations can reduce this risk but are challenging to implement in LMICs owing to the multitude of at-risk infants and lack of trained ophthalmologists. Objective To implement an ROP risk model using retinal images from a single baseline examination to identify infants who will develop treatment-requiring (TR)-ROP in LMIC telemedicine programs. Design, Setting, and Participants In this diagnostic study conducted from February 1, 2019, to June 30, 2021, retinal fundus images were collected from infants as part of an Indian ROP telemedicine screening program. An artificial intelligence (AI)-derived vascular severity score (VSS) was obtained from images from the first examination after 30 weeks' postmenstrual age. Using 5-fold cross-validation, logistic regression models were trained on 2 variables (gestational age and VSS) for prediction of TR-ROP. The model was externally validated on test data sets from India, Nepal, and Mongolia. Data were analyzed from October 20, 2021, to April 20, 2022. Main Outcomes and Measures Primary outcome measures included sensitivity, specificity, positive predictive value, and negative predictive value for predictions of future occurrences of TR-ROP; the number of weeks before clinical diagnosis when a prediction was made; and the potential reduction in number of examinations required. Results A total of 3760 infants (median [IQR] postmenstrual age, 37 [5] weeks; 1950 male infants [51.9%]) were included in the study. The diagnostic model had a sensitivity and specificity, respectively, for each of the data sets as follows: India, 100.0% (95% CI, 87.2%-100.0%) and 63.3% (95% CI, 59.7%-66.8%); Nepal, 100.0% (95% CI, 54.1%-100.0%) and 77.8% (95% CI, 72.9%-82.2%); and Mongolia, 100.0% (95% CI, 93.3%-100.0%) and 45.8% (95% CI, 39.7%-52.1%). With the AI model, infants with TR-ROP were identified a median (IQR) of 2.0 (0-11) weeks before TR-ROP diagnosis in India, 0.5 (0-2.0) weeks before TR-ROP diagnosis in Nepal, and 0 (0-5.0) weeks before TR-ROP diagnosis in Mongolia. If low-risk infants were never screened again, the population could be effectively screened with 45.0% (India, 664/1476), 38.4% (Nepal, 151/393), and 51.3% (Mongolia, 266/519) fewer examinations required. Conclusions and Relevance Results of this diagnostic study suggest that there were 2 advantages to implementation of this risk model: (1) the number of examinations for low-risk infants could be reduced without missing cases of TR-ROP, and (2) high-risk infants could be identified and closely monitored before development of TR-ROP.

中文翻译：

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使用人工智能在 3 个低收入和中等收入人群中对早产儿视网膜病变筛查模型进行外部验证。

重要性 早产儿视网膜病变 (ROP) 是可预防性失明的主要原因，对低收入和中等收入国家 (LMIC) 出生的儿童影响尤为严重。面对面和远程医疗筛查检查可以降低这种风险，但由于存在大量高危婴儿且缺乏训练有素的眼科医生，因此在中低收入国家实施起来具有挑战性。目的 使用单次基线检查的视网膜图像来实施 ROP 风险模型，以识别中低收入国家远程医疗计划中将出现需要治疗 (TR)-ROP 的婴儿。设计、设置和参与者 在这项于 2019 年 2 月 1 日至 2021 年 6 月 30 日进行的诊断研究中，作为印度 ROP 远程医疗筛查计划的一部分，收集了婴儿的视网膜眼底图像。从月经后 30 周后第一次检查的图像中获得人工智能 (AI) 得出的血管严重程度评分 (VSS)。使用 5 倍交叉验证，在 2 个变量（胎龄和 VSS）上训练逻辑回归模型以预测 TR-ROP。该模型在印度、尼泊尔和蒙古的测试数据集上进行了外部验证。数据分析时间为2021年10月20日至2022年4月20日。 主要结果和测量主要结果测量包括预测TR-ROP未来发生的敏感性、特异性、阳性预测值和阴性预测值；做出预测时临床诊断前的周数；以及所需检查数量的潜在减少。结果 共有 3760 名婴儿（中位 [IQR] 经后年龄，37 [5] 周；1950 名男婴儿 [51.9%]）被纳入研究。诊断模型对于每个数据集的敏感性和特异性分别如下： 印度，100.0%（95% CI，87.2%-100.0%）和 63.3%（95% CI，59.7%-66.8%）；尼泊尔，100.0%（95% CI，54.1%-100.0%）和 77.8%（95% CI，72.9%-82.2%）；蒙古，100.0%（95% CI，93.3%-100.0%）和 45.8%（95% CI，39.7%-52.1%）。利用 AI 模型，印度 TR-ROP 诊断前 2.0 (0-11) 周、尼泊尔 TR-ROP 诊断前 0.5 (0-2.0) 周和 0 周，TR-ROP 婴儿被识别为 TR-ROP 婴儿。蒙古 TR-ROP 诊断前 (0-5.0) 周。如果低危婴儿不再接受筛查，则可以有效筛查人群，所需检查次数减少 45.0%（印度，664/1476）、38.4%（尼泊尔，151/393）和 51.3%（蒙古，266/519） 。