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» Authors » Sushravya Raghunath

Sushravya Raghunath

Explore the profile of Sushravya Raghunath including associated specialties, affiliations and a list of published articles. Areas
Snapshot
Articles 17
Citations 541
Followers 0
Related Specialties
Pulmonary Medicine
Radiology
Cardiology & Vascular Diseases
Top 10 Co-Authors
Brian J Bartholmai
Ronald A Karwoski
Christopher M Haggerty
Richard A Robb
Brandon K Fornwalt
Srinivasan Rajagopalan
Joseph B Leader
Linyuan Jing
Dustin N Hartzel
David P vanMaanen
Published In
J Thorac Imaging
J Thorac Oncol
Circulation
Med Image Comput Comput Assist Interv
Eur Respir J
Affiliations
Soon will be listed here.
Recent Articles
1.
An ECG-based machine learning model for predicting new-onset atrial fibrillation is superior to age and clinical features in identifying patients at high stroke risk
Raghunath S, Pfeifer J, Kelsey C, Nemani A, Ruhl J, Hartzel D, et al.
J Electrocardiol . 2022 Nov; 76:61-65. PMID: 36436476
Background: Several large trials have employed age or clinical features to select patients for atrial fibrillation (AF) screening to reduce strokes. We hypothesized that a machine learning (ML) model trained...
2.
rECHOmmend: An ECG-Based Machine Learning Approach for Identifying Patients at Increased Risk of Undiagnosed Structural Heart Disease Detectable by Echocardiography
Ulloa-Cerna A, Jing L, Pfeifer J, Raghunath S, Ruhl J, Rocha D, et al.
Circulation . 2022 May; 146(1):36-47. PMID: 35533093
Background: Timely diagnosis of structural heart disease improves patient outcomes, yet many remain underdiagnosed. While population screening with echocardiography is impractical, ECG-based prediction models can help target high-risk patients. We...
3.
Generalizability and quality control of deep learning-based 2D echocardiography segmentation models in a large clinical dataset
Zhang X, Cerna A, Stough J, Chen Y, Carry B, Alsaid A, et al.
Int J Cardiovasc Imaging . 2022 Feb; 38(8):1685-1697. PMID: 35201510
Use of machine learning (ML) for automated annotation of heart structures from echocardiographic videos is an active research area, but understanding of comparative, generalizable performance among models is lacking. This...
4.
Deep Neural Networks Can Predict New-Onset Atrial Fibrillation From the 12-Lead ECG and Help Identify Those at Risk of Atrial Fibrillation-Related Stroke
Raghunath S, Pfeifer J, Ulloa-Cerna A, Nemani A, Carbonati T, Jing L, et al.
Circulation . 2021 Feb; 143(13):1287-1298. PMID: 33588584
Background: Atrial fibrillation (AF) is associated with substantial morbidity, especially when it goes undetected. If new-onset AF could be predicted, targeted screening could be used to find it early. We...
5.
Deep-learning-assisted analysis of echocardiographic videos improves predictions of all-cause mortality
Cerna A, Jing L, Good C, vanMaanen D, Raghunath S, Suever J, et al.
Nat Biomed Eng . 2021 Feb; 5(6):546-554. PMID: 33558735
Machine learning promises to assist physicians with predictions of mortality and of other future clinical events by learning complex patterns from historical data, such as longitudinal electronic health records. Here...
6.
Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network
Raghunath S, Cerna A, Jing L, vanMaanen D, Stough J, Hartzel D, et al.
Nat Med . 2020 May; 26(6):886-891. PMID: 32393799
The electrocardiogram (ECG) is a widely used medical test, consisting of voltage versus time traces collected from surface recordings over the heart. Here we hypothesized that a deep neural network...
7.
A Machine Learning Approach to Management of Heart Failure Populations
Jing L, Cerna A, Good C, Sauers N, Schneider G, Hartzel D, et al.
JACC Heart Fail . 2020 May; 8(7):578-587. PMID: 32387064
Background: Heart failure is a prevalent, costly disease for which new value-based payment models demand optimized population management strategies. Objectives: This study sought to generate a strategy for managing populations...
8.
Prevalence and Electronic Health Record-Based Phenotype of Loss-of-Function Genetic Variants in Arrhythmogenic Right Ventricular Cardiomyopathy-Associated Genes
Carruth E, Young W, Beer D, James C, Calkins H, Jing L, et al.
Circ Genom Precis Med . 2019 Oct; 12(11):e002579. PMID: 31638835
Background: Arrhythmogenic right ventricular cardiomyopathy (ARVC) is associated with variants in desmosome genes. Secondary findings of pathogenic/likely pathogenic variants, primarily loss-of-function (LOF) variants, are recommended for clinical reporting; however, their...
9.
Short-term Automated Quantification of Radiologic Changes in the Characterization of Idiopathic Pulmonary Fibrosis Versus Nonspecific Interstitial Pneumonia and Prediction of Long-term Survival
De Giacomi F, Raghunath S, Karwoski R, Bartholmai B, Moua T
J Thorac Imaging . 2017 Dec; 33(2):124-131. PMID: 29219887
Purpose: Fibrotic interstitial lung diseases presenting with nonspecific and overlapping radiologic findings may be difficult to diagnose without surgical biopsy. We hypothesized that baseline quantifiable radiologic features and their short-term...
10.
Noninvasive risk stratification of lung adenocarcinoma using quantitative computed tomography
Raghunath S, Maldonado F, Rajagopalan S, Karwoski R, DePew Z, Bartholmai B, et al.
J Thorac Oncol . 2014 Aug; 9(11):1698-703. PMID: 25170645
Introduction: Lung cancer remains the leading cause of cancer-related deaths in the United States and worldwide. Adenocarcinoma is the most common type of lung cancer and encompasses lesions with widely...