基于双模态平面CT和超声波图像的肝球菌病的深度学习诊断:一个大规模的,多中心的诊断研究
Jie Zhang1, Jihao Zhang1, Haoze Tang2
1Department of Hepatobiliary Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China.
International journal of surgery (London, England)
|May 13, 2025
概括
结合CT和超声波的新型多式深度学习系统显著改善了肝肠球菌病 (HCE) 和其他肝脏疾病的诊断. 这种先进的成像方法比单一的方法或医生诊断更准确,特别是在资源不足的地区.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 肝病学 肝病学是一种肝病学.
背景情况:
- 在资源不足的地区,肝胆固醇球菌 (HCE) 查的准确性有限.
- 使用计算机断层扫描 (CT) 和超声波开发了一种新的多式成像系统 (HEAC).
- 在资源有限的环境中,HEAC旨在改善HCE检测.
研究的目的:
- 开发和验证综合超声波和CT数据的多模式深度学习系统.
- 为了区分肝脏菌,肝脏囊,肝脏,和健康的肝脏状况.
- 在具有挑战性的环境中,提高肝病的诊断准确性和可靠性.
主要方法:
- 使用EfficientNet3D (CT) 和EfficientNet-B0 (超声波) 创建了一个深度学习模型.
- 利用了来自中国新疆八家医院的8979例病例 (追溯和前) 的数据集.
- 该模型经过了内部,外部和前性测试,与经验丰富的医生进行了性能比较.
主要成果:
- 合并的CT和超声波模型始终优于个体模式和医生诊断.
- 在前性测试中,融合模型实现了0.816准确度,0.849灵敏度,0.942特异性和0.963AUC.
- 七个中心的外部测试显示了类似的高性能,整体准确率为0.849,AUC为0.961.
结论:
- 多模式深度学习系统显著提高了HCE,肝囊和的诊断准确度.
- 这种方法减少了错误诊断率,并提高了诊断可靠性,而不是单一模式的方法和医生诊断.
- 多式联络成像系统在改善医疗保健的可及性和在资源较少的地区的结果方面显示出很大的前景.
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