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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.
Scaling Up Kangaroo Mother Care Through a Facility Delivery Model in Rural Districts of Pakistan: Protocol for a Mixed Methods Study.
JMIR Res Protoc
January 2025
Centre of Excellence in Women and Child Health, Aga Khan University, Karachi, Pakistan.
Background: The neonatal mortality rate in Pakistan is the third highest in Asia, with 8.6 million preterm babies. These newborns require warmth, nutrition, and infection protection, typically provided by incubators.
View Article and Find Full Text PDFAssessing Physician Motivation to Engage in Continuing Professional Development on Artificial Intelligence.
J Contin Educ Health Prof
January 2025
Dr. Adam G. Gavarkovs: Research Associate, Division of Continuing Professional Development, Faculty of Medicine, University of British Columbia.
To realize the transformative potential of artificial intelligence (AI) in health care, physicians must learn how to use AI-based tools effectively, safely, and equitably. Continuing professional development (CPD) activities are one way to learn how to do this. The purpose of this article is to describe a theory-based approach for assessing health professionals' motivation to participate in CPD on AI-based tools.
View Article and Find Full Text PDFObjectives: To explore the challenges and opportunities for research capacity development in the sports chiropractic field.
Methods: A qualitative description study was conducted using semi-structured interviews with 20 sports chiropractic researchers from eight countries and focus group interviews with 12 sports chiropractic leaders from Canada.
Results: Challenges and opportunities for research capacity development were identified within four main themes - 1) affiliations and collaborations, 2) human resources, 3) financial resources, and 4) operational resources.
Background: Mental health remains among the top 10 leading causes of disease burden globally, and there is a significant treatment gap due to limited resources, stigma, limited accessibility, and low perceived need for treatment. Problem Management Plus, a World Health Organization-endorsed brief psychological intervention for mental health disorders, has been shown to be effective and cost-effective in various countries globally but faces implementation challenges, such as quality control in training, supervision, and delivery. While digital technologies to foster mental health care have the potential to close treatment gaps and address the issues of quality control, their development requires context-specific, interdisciplinary, and participatory approaches to enhance impact and acceptance.
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