Posts Tagged ‘Deep Learning’
Artificial intelligence-guided detection of under-recognized cardiomyopathies on point-of-care cardiac ultrasound: a multi-center study.
Link: https://doi.org/10.1101/2024.03.10.24304044 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 developed…
Read MoreAccurate prediction of neurologic changes in critically ill infants using pose AI.
Link: https://doi.org/10.1101/2024.04.17.24305953 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 exam, which can be intermittent and subjective. Reliable,…
Read MorePublication: 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: 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,…
Read MorePediatric ECG-Based Deep Learning to Predict Left Ventricular Dysfunction and Remodeling.
Link: https://doi.org/10.1161/CIRCULATIONAHA.123.067750 Authors: Mayourian, Joshua; La Cava, William G; Vaid, Akhil; Nadkarni, Girish N; Ghelani, Sunil J; Mannix, Rebekah; Geva, Tal; Dionne, Audrey; Alexander, Mark E; Duong, Son Q; Triedman, John K Abstract: Artificial intelligence-enhanced ECG analysis shows promise to detect ventricular dysfunction and remodeling in adult populations. However, its application to pediatric populations remains…
Read MoreDerivation, External Validation and Clinical Implications of a deep learning approach for intracranial pressure estimation using non-cranial waveform measurements.
Link: https://doi.org/10.1101/2024.01.30.24301974 Authors: Gulamali, Faris; Jayaraman, Pushkala; Sawant, Ashwin S; Desman, Jacob; Fox, Benjamin; Chang, Annie; Soong, Brian Y; Arivazaghan, 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 MoreQuantitative Prediction of Right Ventricular Size and Function From the ECG.
Link: https://doi.org/10.1161/JAHA.123.031671 Authors: Duong, Son Q; Vaid, Akhil; My, Vy Thi Ha; Butler, Liam R; Lampert, Joshua; Pass, Robert H; Charney, Alexander W; Narula, Jagat; Khera, Rohan; Sakhuja, Ankit; Greenspan, Hayit; Gelb, Bruce D; Do, Ron; Nadkarni, Girish N Abstract: Right ventricular ejection fraction (RVEF) and end-diastolic volume (RVEDV) are not readily assessed through traditional…
Read MoreMachine learning models to predict end-stage kidney disease in chronic kidney disease stage 4.
Link: https://doi.org/10.1186/s12882-023-03424-7 Authors: Takkavatakarn, Kullaya; Oh, Wonsuk; Cheng, Ella; Nadkarni, Girish N; Chan, Lili Abstract: End-stage kidney disease (ESKD) is associated with increased morbidity and mortality. Identifying patients with stage 4 CKD (CKD4) at risk of rapid progression to ESKD remains challenging. Accurate prediction of CKD4 progression can improve patient outcomes by improving advanced care…
Read MoreDeep Learning and Gastric Cancer: Systematic Review of AI-Assisted Endoscopy.
Link: https://doi.org/10.3390/diagnostics13243613 Authors: Klang, Eyal; Sourosh, Ali; Nadkarni, Girish N; Sharif, Kassem; Lahat, Adi Abstract: Gastric cancer (GC), a significant health burden worldwide, is typically diagnosed in the advanced stages due to its non-specific symptoms and complex morphological features. Deep learning (DL) has shown potential for improving and standardizing early GC detection. This systematic review…
Read MoreValidation of a convolutional neural network that reliably identifies electromyographic compound motor action potentials following train-of-four stimulation: an algorithm development experimental study.
Link: https://doi.org/10.1016/j.bjao.2023.100236 Authors: Epstein, Richard H; Perez, Olivia F; Hofer, Ira S; Renew, J Ross; Brull, Sorin J; Nemes, Réka Abstract: International guidelines recommend quantitative neuromuscular monitoring when administering neuromuscular blocking agents. The train-of-four count is important for determining the depth of block and appropriate reversal agents and doses. However, identifying valid compound motor action…
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