基于超声波图像和临床数据的多模式深度学习,以更好地诊断卵巢癌
Chang Su1, Kuo Miao1, Liwei Zhang1
1Department of Ultrasound, Fourth Affiliated Hospital of Harbin Medical University, 37#Yi Yuan Street, Harbin, 150086, China.
Journal of imaging informatics in medicine
|June 24, 2025
概括
结合超声波图像和临床数据的新型多式深度学习模型显著改善了卵巢癌 (OC) 诊断. 这种人工智能工具提高了放射科医生的诊断准确度和一致性,有助于早期检测.
科学领域:
- 人工智能的人工智能
- 医疗成像医学成像
- 在瘤学瘤学.
背景情况:
- 卵巢癌 (OC) 诊断严重依赖于成像,但准确性可能有所不同.
- 超声波 (美国) 是主要的成像方式,但解释形态特征可能具有挑战性.
- 将临床数据与美国图像集成可能会改善诊断性能.
研究的目的:
- 开发和验证用于卵巢癌 (OC) 诊断的多式深度学习 (DL) 模型.
- 评估模型从美国图像中提取形态特征的能力.
- 将DL模型的性能与仅图像模型和人类阅读器进行比较.
主要方法:
- 对1899名患有附质群体的患者进行了回顾性分析,这些患者接受了手术前的美国和手术.
- 构建一个多模式DL模型,集成2D灰度美国图像和临床数据.
- 使用接收器操作特征 (ROC) 曲线,准确性,F1分数和读者间协议进行评估.
主要成果:
- 多模式DL模型实现了高诊断性能,曲线下的面积 (AUC) 为0.9393 (内部) 和0.9317 (外部).
- 该模型显著改善了放射科医生OC诊断的AUC,并增强了读者之间的协议.
- 美国形态特征的精确提取被证明,准确度为86.34% (内部) 和85.62% (外部).
结论:
- 多模式深度学习显示出显著的潜力,可以提高诊断准确性和卵巢癌检测的一致性.
- 该模型在特征提取方面的能力表明了自动化结构化超声波报告的潜力.
- 这种人工智能方法可以成为放射科医生在改善卵巢癌诊断方面的一种有价值的工具.
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