Chang Jung Min, Lee Weonsuk, Bahl Manisha
J Korean Soc Radiol. 2025 Mar;86(2):205-215. doi: 10.3348/jksr.2025.0011. Epub 2025 Mar 26.
Digital breast tomosynthesis (DBT) provides improved cancer detection and lower recall rates when compared with full-field digital mammography (DM) and has been widely adopted for breast cancer screening. However, adopting DBT presents new challenges such as an increased number of acquired images resulting in longer interpretation times. Artificial intelligence (AI) offers numerous opportunities to enhance the advantages of DBT and mitigate its shortcomings. Research in the DBT AI domain has grown significantly and AI algorithms play a key role in the screening and diagnostic phases of breast cancer detection and characterization. The application of AI may streamline the workflow and reduce the time required for radiologists to interpret images. In addition, AI can minimize radiation exposure and enhance lesion visibility in synthetic two-dimensional DM images. This review provides an overview of AI technology in DBT, its clinical applications, and future considerations.
与全视野数字乳腺摄影(DM)相比,数字乳腺断层合成(DBT)能提高癌症检测率并降低召回率,已被广泛用于乳腺癌筛查。然而,采用DBT带来了新的挑战,如采集图像数量增加导致解读时间延长。人工智能(AI)为增强DBT的优势和减轻其缺点提供了众多机会。DBT人工智能领域的研究显著增长,AI算法在乳腺癌检测和特征描述的筛查及诊断阶段发挥着关键作用。AI的应用可以简化工作流程,减少放射科医生解读图像所需的时间。此外,AI可以将辐射暴露降至最低,并提高合成二维DM图像中病变的可见性。本文综述了DBT中的AI技术、其临床应用及未来考量。