Urogenital diseases and disorders are common among men and women and can affect adults and pediatrics. If not detected and diagnosed precisely at an early stage, these diseases will lead to complications, substantial disability, and threaten patients' lives. In addition, an accurate diagnosis of many diseases may require surgical biopsies that are highly invasive. These may lead to adverse effects and complications, as well as a significant amount of expenditures.
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
Urogenital diseases and disorders are common among men and women and can affect adults and pediatrics. If not detected and diagnosed precisely at an early stage, these diseases will lead to complications, substantial disability, and threaten patients' lives. In addition, an accurate diagnosis of many diseases may require surgical biopsies that are highly invasive. These may lead to adverse effects and complications, as well as a significant amount of expenditures.
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
