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Background: Vast volumes of routinely collected data (RCD) about patients are collated by health professionals. Leveraging this data - a form of real-world data - can be valuable for quality improvement and contributing to the evidence-base to inform practice. Examining routine data may be especially useful for examining issues related to social justice such as health inequities.
View Article and Find Full Text PDFDeveloping multifactorial dementia prediction models using clinical variables from cohorts in the US and Australia.
Transl Psychiatry
January 2025
Translational Dementia Research Group, Centre for Immunology and Allergy Research, Westmead Institute for Medical Research, Sydney, NSW, 2145, Australia.
Existing dementia prediction models using non-neuroimaging clinical measures have been limited in their ability to identify disease. This study used machine learning to re-examine the diagnostic potential of clinical measures for dementia. Data was sourced from the Australian Imaging, Biomarkers, and Lifestyle Flagship Study of Ageing (AIBL) and the Alzheimer's Disease Neuroimaging Initiative (ADNI).
View Article and Find Full Text PDFApproximately 800,000 total knee arthroplasties and 450,000 total hip arthroplasties are performed annually in the United States. These procedures provide significant pain relief and restore function in patients with advanced osteoarthritis, rheumatoid arthritis, and other degenerative joint conditions. Patient evaluation before surgery includes a history, physical examination, laboratory tests, and imaging.
View Article and Find Full Text PDFUtilizing Image Processing Techniques for Wound Management and Evaluation in Clinical Practice: Establishing the Feasibility of Implementing Artificial Intelligence in Routine Wound Care.
Adv Skin Wound Care
January 2025
Mai Dabas is Master's Degree Student, Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel. Suzanne Kapp, PhD, RN, is Clinical Associate Professor, School of Health Sciences, Faculty of Medicine, Dentistry and Health Sciences, Department of Nursing, The University of Melbourne, Melbourne, Australia; and National Manager Wound Prevention and Management, Regis Aged Care, Camberwell, Victoria, Australia. Amit Gefen, PhD, is Professor of Biomedical Engineering and the Herbert J. Berman Chair in Vascular Bioengineering, Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel; Skin Integrity Research Group (SKINT), University Centre for Nursing and Midwifery, Department of Public Health and Primary Care, Ghent University, Ghent, Belgium; and Department of Mathematics and Statistics and the Data Science Institute, Faculty of Sciences, Hasselt University, Hasselt, Belgium. Acknowledgments: This work was supported by a competitive grant from the Victorian Medical Research Acceleration Fund, with funding co-contribution from the Department of Nursing at the University of Melbourne, the Melbourne Academic Centre for Health, and Mölnlycke Health Care. This work was also partially supported by the Israeli Ministry of Science & Technology (Medical Devices Program grant no. 3-17421, awarded to Professor Amit Gefen in 2020). The authors thank Ms Carla Bondini for her assistance with data collection and management for this study and Mr Daniel Kapp for proofreading the manuscript. The authors have disclosed no other financial relationships related to this article. Submitted February 1, 2024; accepted in revised form April 16, 2024.
Objective: To develop a generalizable and accurate method for automatically analyzing wound images captured in clinical practice and extracting key wound characteristics such as surface area measurement.
Methods: The authors used image processing techniques to create a robust algorithm for segmenting pressure injuries from digital images captured by nurses during clinical practice. The algorithm also measured the real-world wound surface area.
Generative Artificial Intelligence in Anatomic Pathology.
Arch Pathol Lab Med
January 2025
the Department of Pathology, The Ohio State University, Columbus (Parwani).
Context.—: Generative artificial intelligence (AI) has emerged as a transformative force in various fields, including anatomic pathology, where it offers the potential to significantly enhance diagnostic accuracy, workflow efficiency, and research capabilities.
Objective.
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