山东大学学报 (医学版) ›› 2024, Vol. 62 ›› Issue (1): 38-47.doi: 10.6040/j.issn.1671-7554.0.2023.0435
• 临床医学 • 上一篇
张景慧1,王娟2,赵玉洁3,段淼1,刘毅然4,林敏娟1,谯旭4,李真1,左秀丽1
ZHANG Jinghui1, WANG Juan2, ZHAO Yujie3, DUAN Miao1, LIU Yiran4, LIN Minjuan1, QIAO Xu4, LI Zhen1, ZUO Xiuli1
摘要: 目的 构建基于机器学习的胃肠道疾病舌诊模型,以寻求更加方便、经济的方式实现对常见胃肠道疾病的非侵入性诊断。 方法 前瞻性收集接受电子内镜检查的948名受试者的舌象图片,经过质量筛选,最终获得符合应用标准的3 140张图片构成本研究使用的舌象数据集。对原始舌象数据进行预处理、特征提取与模式识别,在传统机器学习方法的基础之上,提出一种从特征融合和决策融合两个方面实现信息融合的方法,以此构建以舌象特征为输入的胃肠道疾病舌诊模型。 结果 本研究构建的基于舌象的信息融合诊断模型的曲线下面积(area under the curve, AUC)为0.808,高于单一手工特征(AUC=0.769)和深度特征(AUC=0.779)模型;使用BSFCM混合采样方法进行样本增强提高了该模型对幽门螺杆菌(Helicobacter pylori, H.pylori)感染(AUC=0.816)、胆汁反流(AUC=0.829)、反流性食管炎(AUC=0.800)、胃糜烂(AUC=0.833)和十二指肠糜烂(AUC=0.818)的分类性能。 结论 本研究构建的基于机器学习的智能舌诊模型对多种胃肠道疾病具有较高的区分度,或为胃肠道疾病的诊断与筛查提供一种新的、有价值的思路与方法。
中图分类号:
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