Department of Radiology, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-cho, Chuo-ku, Kobe-City, Hyogo, 650-0017, Japan.
Department of Radiology, Osaka University Graduate School of Medicine, Suita-City, Osaka, 565-0871, Japan.
Radiol Med. 2024 Sep;129(9):1275-1287. doi: 10.1007/s11547-024-01861-4. Epub 2024 Aug 3.
Magnetic resonance imaging (MRI) is an essential tool for evaluating pelvic disorders affecting the prostate, bladder, uterus, ovaries, and/or rectum. Since the diagnostic pathway of pelvic MRI can involve various complex procedures depending on the affected organ, the Reporting and Data System (RADS) is used to standardize image acquisition and interpretation. Artificial intelligence (AI), which encompasses machine learning and deep learning algorithms, has been integrated into both pelvic MRI and the RADS, particularly for prostate MRI. This review outlines recent developments in the use of AI in various stages of the pelvic MRI diagnostic pathway, including image acquisition, image reconstruction, organ and lesion segmentation, lesion detection and classification, and risk stratification, with special emphasis on recent trends in multi-center studies, which can help to improve the generalizability of AI.
磁共振成像(MRI)是评估影响前列腺、膀胱、子宫、卵巢和/或直肠的盆腔疾病的重要工具。由于盆腔 MRI 的诊断途径可能因受影响的器官而异,涉及各种复杂的程序,因此使用报告和数据系统(RADS)来标准化图像采集和解释。人工智能(AI)涵盖了机器学习和深度学习算法,已被整合到盆腔 MRI 和 RADS 中,特别是在前列腺 MRI 中。这篇综述概述了人工智能在盆腔 MRI 诊断途径的各个阶段的最新应用进展,包括图像采集、图像重建、器官和病变分割、病变检测和分类以及风险分层,特别强调了多中心研究的最新趋势,这有助于提高 AI 的泛化能力。