Yang Lei, Shen Xiaoyu, Liu Yiman, Chen Jiangang, Zou Yuwen, Xu Lihao, Ji Wei, Zhang Yuqi, Liu Tingliang, Cao Qing
Department of Cardiology, Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China.
Department of Pediatrics, Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China.
Pediatr Cardiol. 2025 Jan 18. doi: 10.1007/s00246-024-03762-9.
Kawasaki disease (KD) is a febrile vasculitis disorder, with coronary artery lesions (CALs) being the most severe complication. Early detection of CALs is challenging due to limitations in echocardiographic equipment (UCG). This study aimed to develop and validate an artificial intelligence algorithm to distinguish CALs in KD patients and support diagnostic decision-making at admission. A deep learning algorithm named KCPREDICT was developed using 24 features, including basic patient information, five classic KD clinical signs, and 14 laboratory measurements. Data were collected from patients diagnosed with KD between February 2017 and May 2023 at Shanghai Children's Medical Center. Patients were split into training and internal validation cohorts at an 80:20 ratio, and fivefold cross-validation was employed to assess model performance. Among the 1474 KD cases, the decision tree model performed best during the full feature experiment, achieving an accuracy of 95.42%, a precision of 98.83%, a recall of 93.58%, an F1 score of 96.14%, and an area under the receiver operating characteristic curve (AUROC) of 96.00%. The KCPREDICT algorithm can aid frontline clinicians in distinguishing KD patients with and without CALs, facilitating timely treatment and prevention of severe complications. The use of the complete set of 24 diagnostic features is the optimal choice for predicting CALs in children with KD.
川崎病（KD）是一种发热性血管炎疾病，冠状动脉病变（CALs）是其最严重的并发症。由于超声心动图设备（UCG）的局限性，早期检测CALs具有挑战性。本研究旨在开发并验证一种人工智能算法，以区分KD患者中的CALs，并在入院时支持诊断决策。使用包括基本患者信息、五种经典KD临床体征和14项实验室测量值在内的24个特征开发了一种名为KCPREDICT的深度学习算法。数据收集自2017年2月至2023年5月在上海儿童医学中心诊断为KD的患者。患者按80:20的比例分为训练队列和内部验证队列，并采用五折交叉验证来评估模型性能。在1474例KD病例中，决策树模型在全特征实验中表现最佳，准确率为95.42%，精确率为98.83%，召回率为93.58%，F1分数为96.14%，受试者操作特征曲线下面积（AUROC）为96.00%。KCPREDICT算法可帮助一线临床医生区分有和没有CALs的KD患者，促进及时治疗并预防严重并发症。使用全套24个诊断特征是预测KD患儿CALs的最佳选择。
