Song Linghao, Miao Ao, Wang Xinyue, Liu Yan, Shen Xin, Chen Zexu, Jia Wannan, Wang Yalei, Chen Xinyao, Chen Tianhui, Jiang Yongxiang
Eye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai, 200031, China.
Key laboratory of Myopia and Related Eye Diseases, NHC; Key laboratory of Myopia and Related Eye Diseases, Chinese Academy of Medical Sciences, Shanghai, 200031, China.
Int J Med Sci. 2025 Jul 28;22(14):3501-3510. doi: 10.7150/ijms.109657. eCollection 2025.
To establish a stacking machine learning model for cardiac phenotype prediction in ectopia lentis (EL) patients on the basis of their genotype and ocular phenotype. We enrolled 151 patients with congenital EL and divided them into three groups according to their echocardiograph (normal group, reflux group, and organic lesion group). All the subjects underwent genetic screening and an up-to-1-year ophthalmic and cardiac follow-up. Patients were randomly divided into training set and validation set in a 3:1 ratio. Six statistically significant parameters based on one-way ANOVA and regression analysis were fed into nine basic algorithms for diagnostic training. Among the three groups, intergroup differences in axial length and central corneal thickness were identified. In genotypes, patients with cysteine-eliminating dominant negative and homozygous deficiency mutations were predisposed to cardiac abnormalities. In addition, the corneal radius of curvature and the mutation domain were also included in the experimental dataset. In the validation set, the diagnostic model achieved a comprehensive accuracy of 75% for predicting cardiac phenotype. We established a reliable machine-learning model which predicts cardiac phenotype using genotype and ocular phenotype in EL patients. This model possibly facilitates effective diagnosis of Marfan syndrome.
基于基因型和眼部表型建立用于预测晶状体异位（EL）患者心脏表型的堆叠机器学习模型。我们招募了151例先天性EL患者，并根据其超声心动图将他们分为三组（正常组、反流组和器质性病变组）。所有受试者均接受了基因筛查以及长达1年的眼科和心脏随访。患者以3:1的比例随机分为训练集和验证集。将基于单因素方差分析和回归分析得出的六个具有统计学意义的参数输入到九种基本算法中进行诊断训练。在三组中，确定了眼轴长度和中央角膜厚度的组间差异。在基因型方面，具有半胱氨酸消除显性阴性和纯合缺失突变的患者易患心脏异常。此外，角膜曲率半径和突变域也被纳入实验数据集。在验证集中，诊断模型预测心脏表型的综合准确率达到75%。我们建立了一个可靠的机器学习模型，该模型利用EL患者的基因型和眼部表型来预测心脏表型。该模型可能有助于马凡综合征的有效诊断。
