Recent trends in AI applications for pelvic MRI: a comprehensive review.
Takahiro TsuboyamaMasahiro YanagawaTomoyuki FujiokaShohei FujitaDaiju UedaRintaro ItoAkira YamadaYasutaka FushimiFuminari TatsugamiTakeshi NakauraTaiki NozakiKoji KamagataYusuke MatsuiKenji HirataNoriyuki FujimaMariko KawamuraShinji NaganawaPublished in: La Radiologia medica (2024)
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.
Keyphrases
- deep learning
- artificial intelligence
- machine learning
- magnetic resonance imaging
- contrast enhanced
- big data
- convolutional neural network
- diffusion weighted imaging
- rectal cancer
- prostate cancer
- computed tomography
- emergency department
- electronic health record
- sensitive detection
- benign prostatic hyperplasia
- quantum dots