A Brief Review of Artificial Intelligence in Genitourinary Oncological Imaging.
Enis Cagatay YilmazMason J BelueBaris TurkbeyCaroline ReinholdPeter L ChoykePublished in: Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes (2022)
Genitourinary (GU) system is among the most commonly involved malignancy sites in the human body. Imaging plays a crucial role not only in diagnosis of cancer but also in disease management and its prognosis. However, interpretation of conventional imaging methods such as CT or MR imaging (MRI) usually demonstrates variability across different readers and institutions. Artificial intelligence (AI) has emerged as a promising technology that could improve the patient care by providing helpful input to human readers through lesion detection algorithms and lesion classification systems. Moreover, the robustness of these models may be valuable in automating time-consuming tasks such as organ and lesion segmentations. Herein, we review the current state of imaging and existing challenges in GU malignancies, particularly for cancers of prostate, kidney and bladder; and briefly summarize the recent AI-based solutions to these challenges.
Keyphrases
- artificial intelligence
- machine learning
- deep learning
- big data
- high resolution
- endothelial cells
- contrast enhanced
- prostate cancer
- magnetic resonance imaging
- squamous cell carcinoma
- spinal cord injury
- induced pluripotent stem cells
- working memory
- rectal cancer
- mass spectrometry
- robot assisted
- photodynamic therapy
- papillary thyroid
- fluorescence imaging
- diffusion weighted imaging
- label free