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基于双模超声波的深度学习放射学,用于区分良性和恶性乳腺病变

Juntao Shen^1,2, Gongquan Chen³, Haimei Lun²

¹Guangxi Medical University, Nanning, China.

|November 11, 2025

概括

一个新的双模深度学习放射学模型使用B模式和对比增强超声波准确地区分乳腺病变. 这种人工智能工具帮助放射科医生,提高诊断性能,并可能减少不必要的活检.

科学领域:

放射学放射学是一门学科.
人工智能的人工智能
医疗成像医学成像

背景情况:

乳腺癌是全球女性的主要恶性瘤.
准确的,非侵入性诊断方法对于尽量减少活检和手术至关重要.
目前的诊断方法需要改进以提高准确性.

研究的目的:

开发一种双模深度学习 (DL) 放射学模型,集成B模式超声波 (BUS) 和对比增强超声波 (CEUS) 图像.
为了增强良性与恶性乳腺病变的分化.
创建一个人工智能工具,支持放射科医生的诊断准确度.

主要方法:

一项对427名女性患者进行的回顾性多中心研究.
使用VGG-16和BUS和CEUS数据的特征融合,开发DL放射学模型.
使用SHAP和Grad-CAM评估模型可解释性,然后进行两轮读者研究.

主要成果:

综合模型在测试队列中达到0.825的高AUC.
对于诊断性能来说,CEUS的功能比公共汽车的功能有更大的影响.
人工智能模型最初的表现优于放射科医生,并且当作为辅助工具使用时,显著提高了放射科医生的表现.

结论:

集成的双模态DL放射学模型为乳腺损伤评估提供了一种有效的非侵入性方法.
这种人工智能工具显示出在改善放射科医生在临床实践中的诊断性能方面的巨大潜力.
该模型可以帮助预测病变恶性病变,支持临床决策.

关键词:

无线电学 (Radiomics) 是一种无线电学.乳腺癌 (BCs) 的发生.与对比度增强的超声波 (CEUS)深度学习 (DL) 是指深度学习.这是一个双模模式的双模模式.

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