Publication
Publication: Novel Intracranial Pressure Monitoring Using Non-Invasive Deep Learning Approach
A research study published as a preprint on February 27, 2024, by the AIMS Lab, showcases an innovative deep-learning approach that could redefine the field of intracranial pressure monitoring. In conditions like severe acute brain injuries (SABIs), including strokes and traumatic brain injuries, accurate measurement of intracranial pressure (ICP) can be significant for managing the…
Read MorePublication: Machine Learning Boosts Precision in Social Determinants of Health Analysis
Area-level social determinants of health (SDOH) based on patients’ ZIP codes or census tracts have been commonly used in research instead of individual SDOHs. However, using area-level SDOH measures as a substitute for individual SDOH measures may not be appropriate, especially in highly diverse urban neighbourhoods like New York City. On February 8, 2024, a…
Read MorePublication: Simulation – What Is the Impact of Predictive AI in the Health Care Setting?
Models built on machine learning in health care can be victims of their own success, according to researchers at the Icahn School of Medicine and the University of Michigan. Their study assessed the impact of implementing predictive models on the subsequent performance of those and other models. Their findings—that using the models to adjust how care is delivered…
Read MorePublication: Deep Learning Insights on RV Function from ECG
In a groundbreaking study published on 29 December 2023 in the Journal of the American Heart Association, AIMS researchers have unveiled a novel approach to assess right ventricular (RV) size and function using deep learning-enabled electrocardiogram (ECG) analysis. Traditionally, assessing metrics like right ventricular ejection fraction (RVEF) and end-diastolic volume (RVEDV) required advanced imaging techniques,…
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