Yenice Esay Kiran, Kara Caner, Yenice Mustafa, Erdas Cagatay Berke
Department of Ophthalmology, University of Health Sciences, Etlik Zübeyde Hanım Maternity and Women's Health Teaching and Research Hospital, Ankara, Türkiye.
Department of Ophthalmology, Etlik City Hospital, Ankara, Türkiye.
Beyoglu Eye J. 2023 Dec 1;8(4):287-292. doi: 10.14744/bej.2023.24008. eCollection 2023.
The objective is to predict the development of retinopathy of prematurity (ROP) in discordant twins using a machine learning approach.
The records of 640 twin pairs born at 32-35 weeks gestational age (GA) with birth weight (BW) discordance were evaluated retrospectively. The infants' gender, GA, postmenstruel age at examination, BW, discordance rate, ROP Stages and Zones, and treatment options were recorded. The variables were used to develop a model to predict the development of ROP. Machine learning models were used for algorithm training and 10-fold cross-validation (CV) was applied for validation. The main measures were reported as sensitivity, specificity, receiver operating characteristic curve, and the area under the curve.
A total of 640 twin pairs underwent ophthalmic examination, of which 55 (4.3%) were ROP. The infants' GA was 33.56±1.01 weeks (32-35 weeks) and BW was 1996±335 g (1000-3400 g). The mean discordance rate of the infants was 11.8±9.7% (0.0-53.9%). Using operating points, the Decision Tree algorithm detected ROP prediction with 71% sensitivity and 80% specificity in CV, while the Multi-Layer Perceptron algorithm detected 70% sensitivity and specificity. In addition, the X-Tree and Random Forest algorithms detected ROP prediction with 84% and 80% specificity, respectively.
The results of this study support that BW discordance may be effective in the development of ROP in preterm twins and that artificial intelligence models can predict the development of ROP in accordance with clinical findings.
采用机器学习方法预测双胎输血综合征（TTTS）中早产儿视网膜病变（ROP）的发展。
回顾性评估640对孕32 - 35周出生、出生体重（BW）不一致的双胎记录。记录婴儿的性别、孕周、检查时的月经后年龄、BW、不一致率、ROP分期和区域以及治疗方案。这些变量用于建立预测ROP发展的模型。使用机器学习模型进行算法训练，并应用10折交叉验证（CV）进行验证。主要测量指标报告为敏感性、特异性、受试者工作特征曲线和曲线下面积。
共640对双胞胎接受了眼科检查，其中55对（4.3%）患有ROP。婴儿的孕周为33.56±1.01周（32 - 35周），BW为1996±335 g（1000 - 3400 g）。婴儿的平均不一致率为11.8±9.7%（0.0 - 53.9%）。使用操作点，决策树算法在CV中检测ROP预测的敏感性为71%，特异性为80%，而多层感知器算法检测到的敏感性和特异性为70%。此外，X树和随机森林算法检测ROP预测的特异性分别为84%和80%。
本研究结果支持BW不一致可能对早产双胎ROP的发展有效，并且人工智能模型可以根据临床发现预测ROP的发展。
