儿童机器学习衍生尾炎评分的诊断准确性:一个多中心验证研究
Emrah Aydın1, Taha Eren Sarnıç2, İnan Utku Türkmen2
1Department of Pediatric Surgery, Tekirdağ Namık Kemal University School of Medicine, Tekirdağ 59030, Turkey.
Children (Basel, Switzerland)
|July 29, 2025
概括
一个新的机器学习模型显著改善了儿科尾炎的诊断. 这种使用常见数据的人工智能工具提供了高准确度,有可能减少儿童的延迟和不必要的成像.
科学领域:
- 儿科医学 儿科医学
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
背景情况:
- 由于目前的评分系统的各种症状和局限性,对儿童急性尾炎的准确诊断具有挑战性.
- 以前用于尾炎诊断的机器学习 (ML) 研究往往受到小样本大小,单中心数据和缺乏外部验证的限制.
研究的目的:
- 开发和验证基于机器学习的儿童尾炎诊断模型,使用常规可用的临床和血液学参数.
- 将ML模型的性能与传统的评分系统进行比较,例如儿科尾炎评分 (PAS),阿尔瓦拉多和尾炎炎炎症反应评分 (AIRS).
主要方法:
- 一项前性多中心研究,涉及8586名儿科患者,用于模型开发.
- 外部验证是在一个单独的,前性收集的3000名患者队列上进行的.
- 使用随机森林算法,评估了诊断准确度,灵敏度,特异性和曲线下的面积 (AUC).
主要成果:
- 与传统的临床评分相比,ML模型在开发和验证队列中表现出优异的性能.
- 在外部验证组中,随机森林模型实现了AUC为0.996,准确度为0.992,灵敏度为0.998,特异性为0.993.
- 通过特征重要性分析确定的关键预测因素包括白细胞计数,红细胞计数和平均血小板体积.
结论:
- 使用可访问数据的机器学习评分系统显著提高了儿科尾炎的诊断.
- 开发的模型具有很高的准确性和临床解释性,有可能最大限度地减少诊断延迟,并减少对儿童不必要的成像的需求.
- 这项大规模,前性验证的研究支持ML在改善儿科尾炎诊断方面的临床实用性.
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