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AI-based analysis of fetal growth restriction in a prospective obstetric cohort quantifies compound risks for perinatal morbidity and mortality and identifies previously unrecognized high risk clinical scenarios.
BMC Pregnancy Childbirth
January 2025
Department of Obstetrics and Gynecology, Division of Maternal-Fetal Medicine, University of Utah Health, 30 N. Mario Capecchi Dr., Level 5 South, Salt Lake City, UT, 84132, USA.
Background: Fetal growth restriction (FGR) is a leading risk factor for stillbirth, yet the diagnosis of FGR confers considerable prognostic uncertainty, as most infants with FGR do not experience any morbidity. Our objective was to use data from a large, deeply phenotyped observational obstetric cohort to develop a probabilistic graphical model (PGM), a type of "explainable artificial intelligence (AI)", as a potential framework to better understand how interrelated variables contribute to perinatal morbidity risk in FGR.
Methods: Using data from 9,558 pregnancies delivered at ≥ 20 weeks with available outcome data, we derived and validated a PGM using randomly selected sub-cohorts of 80% (n = 7645) and 20% (n = 1,912), respectively, to discriminate cases of FGR resulting in composite perinatal morbidity from those that did not.
Introduction: Understanding causal risk factors that contribute to the development of multimorbidity is essential for designing and targeting effective preventive strategies. Despite a large body of research in this field, there has been little critical discussion about the appropriateness of the various analytical approaches used. This proposed scoping review aims to summarise and appraise the analytical approaches used in the published literature that evaluated risk factors of multimorbidity and to provide guidance for researchers conducting analyses in this field.
View Article and Find Full Text PDFThe impact of climate change on vulnerable populations in pediatrics: opportunities for AI, digital health, and beyond-a scoping review and selected case studies.
Pediatr Res
January 2025
Division of General Pediatrics, Department of Pediatrics, The Children's Hospital of Philadelphia, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA.
Climate change critically impacts global pediatric health, presenting unique and escalating challenges due to children's inherent vulnerabilities and ongoing physiological development. This scoping review intricately intertwines the spheres of climate change, pediatric health, and Artificial Intelligence (AI), with a goal to elucidate the potential of AI and digital health in mitigating the adverse child health outcomes induced by environmental alterations, especially in Low- and Middle-Income Countries (LMICs). A notable gap is uncovered: literature directly correlating AI interventions with climate change-impacted pediatric health is scant, even though substantial research exists at the confluence of AI and health, and health and climate change respectively.
View Article and Find Full Text PDFCompositIA: an open-source automated quantification tool for body composition scores from thoraco-abdominal CT scans.
Eur Radiol Exp
January 2025
Imaging Institute of Southern Switzerland, Ente Ospedaliero Cantonale, Lugano, Switzerland.
Background: Body composition scores allow for quantifying the volume and physical properties of specific tissues. However, their manual calculation is time-consuming and prone to human error. This study aims to develop and validate CompositIA, an automated, open-source pipeline for quantifying body composition scores from thoraco-abdominal computed tomography (CT) scans.
View Article and Find Full Text PDFFeasibility of generating sagittal radiographs from coronal views using GAN-based deep learning framework in adolescent idiopathic scoliosis.
Eur Radiol Exp
January 2025
IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.
Background: Minimizing radiation exposure is crucial in monitoring adolescent idiopathic scoliosis (AIS). Generative adversarial networks (GANs) have emerged as valuable tools being able to generate high-quality synthetic images. This study explores the use of GANs to generate synthetic sagittal radiographs from coronal views in AIS patients.
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