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一个支持人工智能的综合性乳房超声波诊断系统,适用于没有超声波仪或放射科医生的低资源环境
Emilio J Ochoa1, Luis C Revilla2, Stefano E Romero2
1Department of Biomedical Engineering, University of Rochester, Rochester, NY, USA.
Scientific reports
|December 19, 2025
概括
一个由人工智能驱动的乳房超声波系统 (美国) 提高了在低资源环境中的乳腺癌早期检测. 这种自动化工具通过分析体积扫描成像 (VSI-B) 数据来改善诊断,减少了对专家解释的需求.
科学领域:
- 医学成像和诊断 医学成像和诊断
- 医疗保健中的人工智能
- 在瘤学瘤学.
背景情况:
- 乳腺癌是妇女死亡的主要原因,早期发现对生存至关重要.
- 作为黄金标准的乳房造影,在资源较低的环境中,其可访问性有限.
- 虽然超声波 (美国) 是一种替代方案,但它需要训练有素的专家,体积扫描成像 (VSI) 采集需要专家解释.
研究的目的:
- 开发一个完全自动的人工智能 (AI) 驱动的系统,用于乳房体积扫描成像 (VSI-B) 解释.
- 创建一个可访问的诊断工具,用于乳腺病变的检测和分类在低资源环境.
- 减少对专家放射科医生的依赖,以使用人工智能诊断乳腺癌.
主要方法:
- 开发了一个人工智能系统,集成了Attention U-Net 3D用于细分和DenseNet用于从VSI-B视频中对乳腺病变进行分类.
- 实施了一种关键框架选择方法,用于增强质量识别,以及用于优化损伤分类的多数投票算法.
- 在98名患者的数据集上使用灵敏度,特异性,精度和曲线下的面积 (AUC) 评估系统性能.
主要成果:
- 检测步骤实现了100%的灵敏度和93.6%的癌症检测特异性.
- 该分类模型显示了87%的准确性,100%的灵敏性和83%的质量识别特异性.
- 多数投票算法优化了分类,产生了0.91.9的AUC.
结论:
- 支持人工智能的VSI-B系统显示出作为服务不足地区乳腺癌可靠诊断工具的巨大潜力.
- 通过集成的AI细分和分类来实现自动化病变检测和分层,可以提高早期诊断.
- 这项技术可以改善临床决策和在资源有限的环境中改善乳腺癌查的可用性.
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