James D Thomas

About

James D Thomas is a Professor of Medicine-Cardiology at Northwestern University, affiliated with the Feinberg School of Medicine. He holds an MD from Harvard Medical School, obtained in 1981, and has completed residency at Massachusetts General Hospital in Cardiology, as well as fellowships at the University of Vermont College of Medicine and Massachusetts General Hospital. His research focuses on cardiovascular disease, with notable contributions in echocardiography, including the comparison of global longitudinal strain measurements among different vendors, and the development of automated software for echocardiographic analysis. His work also encompasses the assessment of paravalvular regurgitation after valve replacement and the application of artificial intelligence in cardiology. Dr. Thomas has authored multiple publications in leading journals, advancing the understanding and evaluation of cardiac function and imaging techniques.

Research topics

• Artificial Intelligence
• Medicine
• Computer Science
• Cardiology
• Machine Learning
• Internal medicine
• Radiology
• Algorithm
• Pathology
• Data science

Selected publications

• Utility of a Deep-Learning Algorithm to Guide Novices to Acquire Echocardiograms for Limited Diagnostic Use

  JAMA Cardiology · 2021 · 407 citations

  Senior authorCorresponding

  Importance: Artificial intelligence (AI) has been applied to analysis of medical imaging in recent years, but AI to guide the acquisition of ultrasonography images is a novel area of investigation. A novel deep-learning (DL) algorithm, trained on more than 5 million examples of the outcome of ultrasonographic probe movement on image quality, can provide real-time prescriptive guidance for novice operators to obtain limited diagnostic transthoracic echocardiographic images. Objective: To test whe…

  PublisherOA PDFDOI

• Deep Learning Algorithm for Automated Cardiac Murmur Detection via a Digital Stethoscope Platform

  Journal of the American Heart Association · 2021 · 125 citations

  Senior authorCorresponding

  Background Clinicians vary markedly in their ability to detect murmurs during cardiac auscultation and identify the underlying pathological features. Deep learning approaches have shown promise in medicine by transforming collected data into clinically significant information. The objective of this research is to assess the performance of a deep learning algorithm to detect murmurs and clinically significant valvular heart disease using recordings from a commercial digital stethoscope platform.…

  DOI

• Deep Learning–Based Automated Echocardiographic Quantification of Left Ventricular Ejection Fraction: A Point-of-Care Solution

  Circulation Cardiovascular Imaging · 2021 · 76 citations

  BACKGROUND: We have recently tested an automated machine-learning algorithm that quantifies left ventricular (LV) ejection fraction (EF) from guidelines-recommended apical views. However, in the point-of-care (POC) setting, apical 2-chamber views are often difficult to obtain, limiting the usefulness of this approach. Since most POC physicians often rely on visual assessment of apical 4-chamber and parasternal long-axis views, our algorithm was adapted to use either one of these 3 views or any c…

  PublisherOA PDFDOI

• GLIDE Score

  JACC. Cardiovascular imaging · 2024-06-05 · 56 citations

  articleOpen access

  Tricuspid valve transcatheter edge-to-edge repair (T-TEER) is the most widely used transcatheter therapy to treat patients with tricuspid regurgitation (TR). The aim of this study was to develop a simple anatomical score to predict procedural outcomes of T-TEER. All patients (n = 168) who underwent T-TEER between January 2017 and November 2022 at 2 centers were included in the derivation cohort. Additionally, 126 patients from 2 separate institutions served as a validation cohort. T-TEER was per…

  PublisherDOI

• Deep Learning for Cardiovascular Imaging

  JAMA Cardiology · 2023 · 56 citations

  Senior authorCorresponding

  Importance: Artificial intelligence (AI), driven by advances in deep learning (DL), has the potential to reshape the field of cardiovascular imaging (CVI). While DL for CVI is still in its infancy, research is accelerating to aid in the acquisition, processing, and/or interpretation of CVI across various modalities, with several commercial products already in clinical use. It is imperative that cardiovascular imagers are familiar with DL systems, including a basic understanding of how they work,…

  PublisherDOI

Recent grants

• NIH Grant M01RR018390

  NIH · $519k · 2007

• NIH Grant R01HL056688

  NIH · $546k · 2001

Frequent coauthors

• Zoran Popović

  Cleveland Clinic

  285 shared

• Neil Greenberg

  King's College London

  275 shared

• Mario J. García

  Albert Einstein College of Medicine

  227 shared

• Arthur E. Weyman

  Massachusetts General Hospital

  216 shared

• Benjamin D. Levine

  The University of Texas Southwestern Medical Center

  198 shared

• Patrick M. McCarthy

  196 shared

• Anand Prasad

  The University of Texas Health Science Center at San Antonio

  177 shared

• Takahiro Shiota

  Cedars-Sinai Medical Center

  161 shared

Similar researchers at Northwestern University

• Tobin J. Marksh-176
• Joseph Hupph-171
• Richard I Morimotoh-155