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Application of artificial intelligence to ultrasound imaging for benign gynecological disorders: systematic review.
Ultrasound Obstet Gynecol
January 2025
Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy.
Objective: Although artificial intelligence (AI) is increasingly being applied to ultrasound imaging in gynecology, efforts to synthesize the available evidence have been inadequate. The aim of this systematic review was to summarize and evaluate the literature on the role of AI applied to ultrasound imaging in benign gynecological disorders.
Methods: Web of Science, PubMed and Scopus databases were searched from inception until August 2024.
Artificial Intelligence in Fetal Growth Restriction Management: A Narrative Review.
J Clin Ultrasound
January 2025
Division of Maternal-Fetal Medicine, Department of Maternal and Fetal Medicine, Obstetrics and Gynecology, University of Miami, Miller School of Medicine, Miami, Florida, USA.
This narrative review examines the integration of Artificial Intelligence (AI) in prenatal care, particularly in managing pregnancies complicated by Fetal Growth Restriction (FGR). AI provides a transformative approach to diagnosing and monitoring FGR by leveraging advanced machine-learning algorithms and extensive data analysis. Automated fetal biometry using AI has demonstrated significant precision in identifying fetal structures, while predictive models analyzing Doppler indices and maternal characteristics improve the reliability of adverse outcome predictions.
View Article and Find Full Text PDFProspective evaluation of artificial intelligence (AI) applications for use in cancer pathways following diagnosis: a systematic review.
BMJ Oncol
May 2024
Institute of Cancer Policy, King's College London Faculty of Life Sciences & Medicine, London, UK.
The role of artificial intelligence (AI) in cancer care has evolved in the face of ageing population, workforce shortages and technological advancement. Despite recent uptake in AI research and adoption, the extent to which it improves quality, efficiency and equity of care beyond cancer diagnostics is uncertain to date. Henceforth, the objective of our systematic review is to assess the clinical readiness and deployability of AI through evaluation of prospective studies of AI in cancer care following diagnosis.
View Article and Find Full Text PDFAbdominal and Pelvic MRI Protocol Prediction Using Natural Language Processing.
J Imaging Inform Med
January 2025
Department of Radiology, Mayo Clinic, Rochester, MN, USA.
Exam protocoling is a significant non-interpretive task burden for radiologists. The purpose of this work was to develop a natural language processing (NLP) artificial intelligence (AI) solution for automated protocoling of standard abdomen and pelvic magnetic resonance imaging (MRI) exams from basic associated order information and patient metadata. This Institutional Review Board exempt retrospective study used de-identified metadata from consecutive adult abdominal and pelvic MRI scans performed at our institution spanning 2.
View Article and Find Full Text PDFArtificial intelligence for segmentation and classification in lumbar spinal stenosis: an overview of current methods.
Eur Spine J
January 2025
Department of Neurosurgery, Leiden University Medical Center, Albinusdreef 2, 2333ZA, Leiden, The Netherlands.
Purpose: Lumbar spinal stenosis (LSS) is a frequently occurring condition defined by narrowing of the spinal or nerve root canal due to degenerative changes. Physicians use MRI scans to determine the severity of stenosis, occasionally complementing it with X-ray or CT scans during the diagnostic work-up. However, manual grading of stenosis is time-consuming and induces inter-reader variability as a standardized grading system is lacking.
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