基于超声波的深度学习模型作为助手改善了卵巢瘤的诊断:一项多中心研究
Yanli Wang1, Jiansong Zhang2, Yifang He1
1Department of Ultrasound, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China.
Insights into imaging
|October 16, 2025
概括
深度学习 (DL) 模型通过超声波图像增强卵巢瘤诊断. 一个集成的DL系统提高了初级医生的诊断准确度到专家水平,有助于早期检测和治疗.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 在瘤学瘤学.
背景情况:
- 超声波 (US) 影像对于诊断卵巢瘤至关重要.
- 深度学习 (DL) 模型有可能提高放射学诊断的准确性.
- 准确的手术前诊断对于有效的卵巢瘤管理至关重要.
研究的目的:
- 开发和评估DL系统,使用美国图像对卵巢瘤进行分类.
- 评估通过DL系统协助的美国初级医生的诊断性能改善.
- 将DL系统的性能与现有的诊断模型和专家进行比较.
主要方法:
- 一项涉及916名患有卵巢瘤的女性的回顾性研究.
- 训练DL模型 (ResNet-50,VGG16,视觉变压器) 在手术前的美国图像上.
- 将美国初级医生的诊断效率与DL辅助和无DL辅助进行比较,包括大型语言模型.
主要成果:
- ResNet-50 DL模型在分类瘤类型方面取得了很高的准确性.
- 在DL系统的帮助下,美国初级医生的诊断准确度显著提高,达到专家水平.
- 美国初级医生的诊断一致性也显示出明显的改善.
结论:
- 结合视觉和语言模型的集成DL系统增强了从美国图像的卵巢瘤分类.
- 开发的DL框架有助于美国初级医生,提高诊断效率,并可能有助于进行手术决策.
- 这项技术可以促进早期检测和及时治疗卵巢瘤.
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