About this Research Topic
Recent advances in the urogenital radiology field can help in an early, non-invasive, and precise diagnosis of such diseases by using different imaging modalities, including, among others, computed tomography (CT), ultrasound (US), magnetic resonance imaging (MRI), and X-rays. The new era of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in medicine has played an important role in the development of new image-based computer-aided diagnostics that may help in the early and precise diagnosis of such diseases.
In this Research Topic, we invite potential contributors to submit their work in all article types (e.g., original research, review, case report, etc.). Research areas may include, but are not limited to, the following:
• MRI in urogenital radiology
• CT in urogenital radiology
• ultrasound in urogenital radiology
• X-rays in urogenital radiology
• computer-aided diagnostics for urogenital diseases
• role of AI in diagnosing urinary tracts
• ML and DL for diagnosing kidney diseases
• AI in diagnosing bladder diseases
• AI in diagnosing prostate diseases
• AI in diagnosing uterus diseases
• diagnosis of adrenal glands using ML and DL
• AI in diagnosing renal cancer
• nuclear medicine techniques
Keywords: urogenital diseases, urinary tract, kidney stones, adrenal glands, renal allograft rejection, renal cancer, bladder cancer, prostate cancer, endometrial carcinoma, uterus diseases, ultrasounds, multiphasic CT, MRIs, X-rays, computer-aided diagnostics, artificial intelligence, machine learning, deep learning
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
