| 张兴梅,张兴华,张刚,郝帅营,李永忠.基于深度学习的乳腺X线肿块自动检测系统诊断乳腺肿块[J].中国医学影像技术,2019,35(12):1794~1798 |
| 基于深度学习的乳腺X线肿块自动检测系统诊断乳腺肿块 |
| Mammography mass detection system based on deep learning in diagnosis of breast masses |
| 投稿时间：2019-08-29 修订日期：2019-11-28 |
| DOI：10.13929/j.1003-3289.201908167 |
| 中文关键词: 乳腺肿瘤 乳房X线摄影术 深度学习 人工智能 |
| 英文关键词:breast neoplasms mammography deep learning artificial intelligence |
| 基金项目: |
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| 中文摘要: |
| 目的 探讨基于深度学习（DL）的乳腺X线肿块自动检测系统诊断乳腺肿块的价值。方法 回顾性分析298例接受乳腺X线检查的女性患者。以3名高年资放射科医师对X线片的评估结果为参照标准，对比分析2名工作时间<5年的放射科医师在无（简称医师1和医师2）或有人工智能（AI）（简称医师1+AI和医师2+AI）辅助下的肿块检出率及检出稳定性。结果 医师1+AI、医师2+AI肿块检出率分别高于医师1、医师2（P均<0.05）。医师+AI检出乳腺肿块不受美国放射学院（ACR）肿块腺体构成、乳腺影像报告和数据系统（BI-RADS）分类及其形状、密度等因素影响（P均>0.05）。结论 基于DL的乳腺X线影像肿块检测系统可有效提高低年资医师的肿块检出率，提升医师对不同类型肿块检出的稳定性。 |
| 英文摘要: |
| Objective To observe the value of a mammogram mass detection system based on deep learning (DL) in diagnosis of breast masses. Methods Data of 298 females who underwent mammography examination were retrospectively analyzed. The reference standards of mass detection were established by three senior radiologists. The lesion detection rate and detection stability of two radiologists with working time less than 5 years were compared and analyzed without (physician 1 and physician 2) or with artificial intelligence (AI) (physician 1+AI and physician 2+AI). Results The lesion detection rate of physician 1+AI and physician 2+AI were all higher than that of physician 1 and physician 2 (both P<0.05). The detection rate of physician+AI was not affected by American college of radiology (ACR) breast density, breast imaging reporting and data system (BI-RADS), mass shape and mass density, etc (all P>0.05). Conclusion The mammogram mass detection system based on DL can effectively improve mass detection rate of junior radiologists, and enhance the robustness of detection rate of different type masses. |
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