Posts Tagged ‘Deep Learning’
Deep Learning-Based Electrocardiogram Analysis Predicts Biventricular Dysfunction and Dilation in Congenital Heart Disease.
Link: https://doi.org/S0735-1097(24)07676-9 Authors: Mayourian, Joshua; Gearhart, Addison; La Cava, William G; Vaid, Akhil; Nadkarni, Girish N; Triedman, John K; Powell, Andrew J; Wald, Rachel M; Valente, Anne Marie; Geva, Tal; Duong, Son Q; Ghelani, Sunil J Abstract: Artificial intelligence-enhanced electrocardiogram (AI-ECG) analysis shows promise to detect biventricular pathophysiology. However, AI-ECG analysis remains underexplored in congenital…
Read MoreArtificial intelligence-guided detection of under-recognised cardiomyopathies on point-of-care cardiac ultrasonography: a multicentre study.
Link: https://doi.org/S2589-7500(24)00249-8 Authors: Oikonomou, Evangelos K; Vaid, Akhil; Holste, Gregory; Coppi, Andreas; McNamara, Robert L; Baloescu, Cristiana; Krumholz, Harlan M; Wang, Zhangyang; Apakama, Donald J; Nadkarni, Girish N; Khera, Rohan Abstract: Point-of-care ultrasonography (POCUS) enables cardiac imaging at the bedside and in communities but is limited by abbreviated protocols and variation in quality. We aimed…
Read MoreThe role of deep learning in diagnostic imaging of spondyloarthropathies: a systematic review.
Link: https://doi.org/10.1007/s00330-024-11261-x Authors: Omar, Mahmud; Watad, Abdulla; McGonagle, Dennis; Soffer, Shelly; Glicksberg, Benjamin S; Nadkarni, Girish N; Klang, Eyal Abstract: Diagnostic imaging is an integral part of identifying spondyloarthropathies (SpA), yet the interpretation of these images can be challenging. This review evaluated the use of deep learning models to enhance the diagnostic accuracy of SpA…
Read MoreDeep Learning for Contrast Enhanced Mammography – A Systematic Review.
Link: https://doi.org/10.1016/j.acra.2024.11.035 Authors: Sorin, Vera; Sklair-Levy, Miri; Glicksberg, Benjamin S; Konen, Eli; Nadkarni, Girish N; Klang, Eyal Abstract: Contrast-enhanced mammography (CEM) is a relatively novel imaging technique that enables both anatomical and functional breast imaging, with improved diagnostic performance compared to standard 2D mammography. The aim of this study is to systematically review the literature…
Read MoreDetection of neurologic changes in critically ill infants using deep learning on video data: a retrospective single center cohort study.
Link: https://doi.org/10.1016/j.eclinm.2024.102919 Authors: Gleason, Alec; Richter, Florian; Beller, Nathalia; Arivazhagan, Naveen; Feng, Rui; Holmes, Emma; Glicksberg, Benjamin S; Morton, Sarah U; La Vega-Talbott, Maite; Fields, Madeline; Guttmann, Katherine; Nadkarni, Girish N; Richter, Felix Abstract: Infant alertness and neurologic changes can reflect life-threatening pathology but are assessed by physical exam, which can be intermittent and subjective.…
Read MoreLeveraging Artificial Intelligence and Data Science for Integration of Social Determinants of Health in Emergency Medicine: Scoping Review.
Link: https://doi.org/10.2196/57124 Authors: Abbott, Ethan E; Apakama, Donald; Richardson, Lynne D; Chan, Lili; Nadkarni, Girish N Abstract: Social determinants of health (SDOH) are critical drivers of health disparities and patient outcomes. However, accessing and collecting patient-level SDOH data can be operationally challenging in the emergency department (ED) clinical setting, requiring innovative approaches. This scoping review…
Read MoreAutomated Identification of Heart Failure With Reduced Ejection Fraction Using Deep Learning-Based Natural Language Processing.
Link: https://doi.org/S2213-1779(24)00618-8 Authors: Nargesi, Arash A; Adejumo, Philip; Dhingra, Lovedeep Singh; Rosand, Benjamin; Hengartner, Astrid; Coppi, Andreas; Benigeri, Simon; Sen, Sounok; Ahmad, Tariq; Nadkarni, Girish N; Lin, Zhenqiu; Ahmad, Faraz S; Krumholz, Harlan M; Khera, Rohan Abstract: The lack of automated tools for measuring care quality limits the implementation of a national program to assess…
Read MoreA Novel Digital Twin Strategy to Examine the Implications of Randomized Clinical Trials for Real-World Populations.
Link: https://doi.org/10.1101/2024.03.25.24304868 Authors: Thangaraj, Phyllis M; Shankar, Sumukh Vasisht; Huang, Sicong; Nadkarni, Girish N; Mortazavi, Bobak J; Oikonomou, Evangelos K; Khera, Rohan Abstract: Randomized clinical trials (RCTs) are essential to guide medical practice; however, their generalizability to a given population is often uncertain. We developed a statistically informed Generative Adversarial Network (GAN) model, RCT-Twin-GAN, that…
Read MoreDerivation, external and clinical validation of a deep learning approach for detecting intracranial hypertension.
Link: https://doi.org/10.1038/s41746-024-01227-0 Authors: Gulamali, Faris; Jayaraman, Pushkala; Sawant, Ashwin S; Desman, Jacob; Fox, Benjamin; Chang, Annette; Soong, Brian Y; Arivazagan, Naveen; Reynolds, Alexandra S; Duong, Son Q; Vaid, Akhil; Kovatch, Patricia; Freeman, Robert; Hofer, Ira S; Sakhuja, Ankit; Dangayach, Neha S; Reich, David S; Charney, Alexander W; Nadkarni, Girish N Abstract: Increased intracranial pressure (ICP)…
Read MoreMultimodal fusion learning for long QT syndrome pathogenic genotypes in a racially diverse population.
Link: https://doi.org/10.1038/s41746-024-01218-1 Authors: Jiang, Joy; Thi Vy, Ha My; Charney, Alexander; Kovatch, Patricia; Reddy, Vivek; Jayaraman, Pushkala; Do, Ron; Khera, Rohan; Chugh, Sumeet; Bhatt, Deepak L; Vaid, Akhil; Lampert, Joshua; Nadkarni, Girish Nitin Abstract: Congenital long QT syndrome (LQTS) diagnosis is complicated by limited genetic testing at scale, low prevalence, and normal QT corrected interval…
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