基于多式联络数据,为患有过敏性鼻炎的儿童开发冷热综合征分类模型
Niancheng Yu1,2, Jian Huang3,4,5, Jia Liu6
1Department of Traditional Chinese Medicine, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China.
Translational pediatrics
|January 8, 2026
概括
一个新的AI模型使用舌头图像和临床数据准确地区分了过敏性鼻炎 (AR) 儿童的感冒和热综合征. 这种方法提高了诊断的准确性,并支持传统中医的治疗决策.
科学领域:
- 人工智能在医学中的应用
- 传统中国医学 诊断 诊断 诊断
- 儿科健康 儿科健康
背景情况:
- 儿童过敏性鼻炎 (AR) 在全球范围内越来越普遍,影响生活质量并造成经济成本.
- 西方医学为AR提供症状缓解,但复发和副作用是令人担忧的.
- 传统中医 (TCM) 通过综合征差异化提供了替代方案,尽管诊断可能是主观的.
研究的目的:
- 开发一种数字舌头图像分析方法,以提高儿科AR的TCM综合征分化的准确性.
- 帮助临床医生在患有AR的儿童中区分感冒和热综合征.
- 提高儿童ARTCM治疗的客观性和有效性.
主要方法:
- 开发了一种多式深度学习模型,整合了来自舌头图像 (SE-DenseNet) 和临床数据的功能.
- 使用变压器模型根据提取的特征将患者分为寒冷或热综合征.
- 模型性能使用包括AUC,准确性,精度,回忆和训练和测试集的F1分数在内的指标进行评估.
主要成果:
- 多模式模型在儿童AR患者冷热综合征的分类方面取得了高性能.
- 在测试组中,该模型报告AUC为0.877,准确度为0.856,精度为0.863,回忆率为0.829,F1得分为0.910.
- 与其他评估模型相比,开发的模型表现出卓越的性能.
结论:
- 多式模式有效地整合了临床和舌头图像特征,用于在儿科AR中准确区分综合征.
- 这种方法提供客观和可量化的诊断结果,可能改善治疗决策.
- 这项研究强调了人工智能辅助TCM诊断在儿科AR管理中的潜力.
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