Wang Shu, Ren Lei, Liu Jihua, Su Cong, He Shanshan, Ren Bo, Yin Jian
The Department of Breast Oncoplastic Surgery, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education; Key Laboratory of Cancer Prevention and Treatment, Tianjin; Tianjin's Clinical Research Center for Cancer; The Sino-Russian Joint Research Center for Oncoplastic Breast Surgery, Tianjin, China.
Medical Imaging Department, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine; National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, China.
JPRAS Open. 2025 Feb 3;44:107-118. doi: 10.1016/j.jpra.2025.01.023. eCollection 2025 Jun.
Three-dimensional (3D) image technology in breast measurement requires exploration. We aimed to evaluate a new automatic breast measurement system based on artificial intelligence (AI).
This prospective controlled study included all-women patients who underwent breast reconstruction from January to May 2022. Patients underwent 3D scanning before breast reconstruction. Two doctors performed the measurements twice through AI and manual measurements on the 3D images, respectively. The measurement results of bilateral breast width, convexity, height, volume, and measurement time were recorded. Consistency analyses were performed.
Fifty-eight patients (116 breasts) were recruited. For the left breasts, AI and manual measurements showed excellent consistency (intra-class correlation coefficients (ICC) = 0.81) in width measurements, moderate consistency (ICC = 0.59) in height measurements, excellent consistency (ICC = 0.87) in convexity measurements, and good consistency (ICC = 0.74) in volume measurements. For the right breasts, the width consistency was excellent (ICC = 0.93), height consistency was good (ICC = 0.65), convexity consistency was excellent (ICC = 0.94), and volume consistency was excellent (ICC = 0.85). The Bland-Altman curves also showed that the measurement results were comparable and few outliers were detected. AI average measurement time (compared to manual measurements) was significantly shorter (40.65 ± 1.51 s vs. 610.47 ± 18.74 s; p < 0.001).
The AI-based 3D breast measurement system showed high accuracy, better reproducibility, and significantly shortened the measurement time, which could help guide surgical management.
乳房测量中的三维(3D)图像技术有待探索。我们旨在评估一种基于人工智能(AI)的新型自动乳房测量系统。
这项前瞻性对照研究纳入了2022年1月至5月接受乳房重建的所有女性患者。患者在乳房重建前进行3D扫描。两名医生分别通过人工智能和手动测量对3D图像进行两次测量。记录双侧乳房宽度、凸度、高度、体积及测量时间的测量结果。进行一致性分析。
招募了58例患者(116个乳房)。对于左侧乳房,人工智能和手动测量在宽度测量上显示出极好的一致性(组内相关系数(ICC)=0.81),在高度测量上显示出中等一致性(ICC = 0.59),在凸度测量上显示出极好的一致性(ICC = 0.87),在体积测量上显示出良好的一致性(ICC = 0.74)。对于右侧乳房,宽度一致性极好(ICC = 0.93),高度一致性良好(ICC = 0.65),凸度一致性极好(ICC = 0.94),体积一致性极好(ICC = 0.85)。Bland-Altman曲线也显示测量结果具有可比性,且检测到的异常值较少。人工智能平均测量时间(与手动测量相比)显著更短(40.65±1.51秒对610.47±18.74秒;p<0.001)。
基于人工智能的3D乳房测量系统显示出高准确性、更好的可重复性,并显著缩短了测量时间,这有助于指导手术管理。