Baek John, Kim Jaeil, Kim Hye Jung, Yoon Jung Hyun, Park Ho Yong, Lee Jeeyeon, Kang Byeongju, Zakiryarov Iliya, Kultaev Askhat, Saktashev Bolat, Kim Won Hwa
J Korean Soc Radiol. 2025 Mar;86(2):216-226. doi: 10.3348/jksr.2025.0019. Epub 2025 Mar 26.
Breast cancer is the most common cancer in women worldwide, and its early detection is critical for improving survival outcomes. As a diagnostic and screening tool, mammography can be less effective owing to the masking effect of fibroglandular tissue, but breast US has good sensitivity even in dense breasts. However, breast US is highly operator dependent, highlighting the need for artificial intelligence (AI)-driven solutions. Unlike other modalities, US is performed using a handheld device that produces a continuous real-time video stream, yielding 12000-48000 frames per examination. This can be significantly challenging for AI development and requires real-time AI inference capabilities. In this review, we classified AI solutions as computer-aided diagnosis and computer-aided detection to facilitate a functional understanding and review commercial software supported by clinical evidence. In addition, to bridge healthcare gaps and enhance patient outcomes in geographically under resourced areas, we propose a novel framework by reviewing the existing AI-based triage workflows including mobile ultrasound.
乳腺癌是全球女性中最常见的癌症,其早期检测对于改善生存结果至关重要。作为一种诊断和筛查工具,由于纤维腺组织的掩盖效应,乳房X线摄影可能效果较差,但即使在乳腺致密的情况下,乳腺超声也具有良好的敏感性。然而,乳腺超声高度依赖操作人员,这凸显了对人工智能(AI)驱动解决方案的需求。与其他模态不同,超声检查使用手持设备进行,该设备会生成连续的实时视频流,每次检查可产生12000-48000帧图像。这对AI开发来说可能具有极大的挑战性,并且需要实时AI推理能力。在本综述中,我们将AI解决方案分为计算机辅助诊断和计算机辅助检测,以便于从功能上理解并审查有临床证据支持的商业软件。此外,为了弥合医疗差距并改善资源匮乏地区患者的治疗结果,我们通过回顾现有的基于AI的分诊工作流程(包括移动超声),提出了一个新颖的框架。