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通过超声波自动实时检测和诊断肝脏瘤

Chih-Horng Wu^1,2, Jin-Chuan Sheu^3,4, Pei-Lien Chou⁵

¹Department of Medical Imaging, National Taiwan University Hospital, Taipei, Taiwan.

Journal of hepatocellular carcinoma

|July 29, 2025

概括

深度学习模型在超声波图像上准确地区分良性和恶性肝病变. 这项技术可以在实时超声波中同时检测和分类病变,以改善肝细胞癌查.

科学领域:

医疗成像医学成像
人工智能的人工智能
肝细胞癌研究肝细胞癌研究

背景情况:

超声波是常见的,但依赖于操作者的肝细胞癌 (HCC) 查工具.
对肝损伤的超声波的诊断性能随操作员的技能而有很大差异.
需要自动化诊断工具来提高准确性和一致性.

研究的目的:

开发和评估深度学习 (DL) 模型,用于肝病变的自动诊断和检测.
评估各种DL模型的诊断性能,以区分恶性和良性肝病变.
为了在实时超声波中实现基于DL的同时病变检测和分类.

主要方法:

通过腹部超声波 (2002-2020年) 诊断出肝癌的1576名患者的回顾性队列被分析.
多个DL模型 (ResNet50,Xception,Inception Resnet V2,EfficientNet变体,Swin变压器变体) 用于诊断.
用YOLOR模型实时检测病变,使用曲线下的面积 (AUC) 和平均平均精度 (mAP) 评估性能.

主要成果:

DL模型实现了高诊断性能,AUC从0.85到0.90不等.
在EfficientNet-B5和Swin-B模型中,AUC最高为0.90.
对于病变检测和差异化,YOLOR模型实现了0.5342 (验证) 和0.5631 (测试) 的mAP分数.

结论:

深度学习模型可以在超声波图像上准确区分良性和恶性肝病变.
基于DL的肝病变的自动检测和分类在实时超声波中是可行的.
这些DL模型有望提高HCC查的一致性和准确性.

关键词:

人工智能的人工智能是人工智能.深度学习是一种深度学习.诊断诊断诊断诊断诊断诊断肝脏瘤是什么意思超声波学超声波学超声波学

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