Sushravya Raghunath

1.8k total citations
27 papers, 822 citations indexed

About

Sushravya Raghunath is a scholar working on Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging and Cardiology and Cardiovascular Medicine. According to data from OpenAlex, Sushravya Raghunath has authored 27 papers receiving a total of 822 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Pulmonary and Respiratory Medicine, 9 papers in Radiology, Nuclear Medicine and Imaging and 8 papers in Cardiology and Cardiovascular Medicine. Recurrent topics in Sushravya Raghunath's work include Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis (7 papers), Radiomics and Machine Learning in Medical Imaging (6 papers) and Lung Cancer Diagnosis and Treatment (6 papers). Sushravya Raghunath is often cited by papers focused on Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis (7 papers), Radiomics and Machine Learning in Medical Imaging (6 papers) and Lung Cancer Diagnosis and Treatment (6 papers). Sushravya Raghunath collaborates with scholars based in United States, Czechia and Italy. Sushravya Raghunath's co-authors include Brian J. Bartholmai, Ronald A. Karwoski, Richard A. Robb, Fabien Maldonado, Srinivasan Rajagopalan, Teng Moua, Paul Decker, Thomas E. Hartman, Srinivasan Rajagopalan and Jay H. Ryu and has published in prestigious journals such as Circulation, PLoS ONE and American Journal of Respiratory and Critical Care Medicine.

In The Last Decade

Sushravya Raghunath

23 papers receiving 810 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Sushravya Raghunath United States	12	605	283	131	107	89	27	822
Lucio Calandriello Italy	14	916 1.5×	377 1.3×	318 2.4×	31 0.3×	83 0.9×	42	1.2k
Stephan Altmayer Brazil	15	312 0.5×	285 1.0×	69 0.5×	67 0.6×	57 0.6×	75	658
Song Soo Kim South Korea	13	378 0.6×	151 0.5×	65 0.5×	73 0.7×	71 0.8×	46	582
Onno M. Mets Netherlands	20	917 1.5×	462 1.6×	110 0.8×	52 0.5×	141 1.6×	44	1.1k
Sang Young Oh South Korea	16	568 0.9×	186 0.7×	75 0.6×	41 0.4×	86 1.0×	49	739
Simon Walsh United Kingdom	11	725 1.2×	193 0.7×	234 1.8×	14 0.1×	42 0.5×	20	868
Jeanne B. Ackman United States	17	232 0.4×	401 1.4×	33 0.3×	39 0.4×	192 2.2×	50	875
Victorine V. Muse United States	17	399 0.7×	500 1.8×	52 0.4×	28 0.3×	200 2.2×	35	890
J W Gurney United States	16	872 1.4×	382 1.3×	43 0.3×	55 0.5×	60 0.7×	26	1.1k
John C. Wandtke United States	14	353 0.6×	171 0.6×	24 0.2×	58 0.5×	52 0.6×	49	527

Countries citing papers authored by Sushravya Raghunath

Since Specialization

Citations

This map shows the geographic impact of Sushravya Raghunath's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Sushravya Raghunath with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sushravya Raghunath more than expected).

Fields of papers citing papers by Sushravya Raghunath

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sushravya Raghunath. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Sushravya Raghunath. The network helps show where Sushravya Raghunath may publish in the future.

Co-authorship network of co-authors of Sushravya Raghunath

This figure shows the co-authorship network connecting the top 25 collaborators of Sushravya Raghunath. A scholar is included among the top collaborators of Sushravya Raghunath based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Sushravya Raghunath. Sushravya Raghunath is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Jing, Linyuan, Thomas Morland, Sushravya Raghunath, et al.. (2025). Machine learning–enabled assessment of risk of drug-induced QT prolongation at the time of prescribing. Heart Rhythm.

Raghunath, Sushravya, John M. Pfeifer, Chris R. Kelsey, et al.. (2022). 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. Journal of Electrocardiology. 76. 61–65. 6 indexed citations

Ulloa, Alvaro, Sushravya Raghunath, David P. vanMaanen, et al.. (2022). Abstract 11000: Deep Learning Prediction of New-Onset Atrial Fibrillation Using Echocardiography Videos. Circulation. 146(Suppl_1).

Zhang, Xiaoyan, Alvaro Ulloa, Joshua V. Stough, et al.. (2022). Generalizability and quality control of deep learning-based 2D echocardiography segmentation models in a large clinical dataset. The International Journal of Cardiovascular Imaging. 38(8). 1685–1697. 3 indexed citations

Ulloa, Alvaro, Linyuan Jing, Christopher W. Good, et al.. (2021). Deep-learning-assisted analysis of echocardiographic videos improves predictions of all-cause mortality. Nature Biomedical Engineering. 5(6). 546–554. 45 indexed citations

Jing, Linyuan, Alvaro Ulloa, Christopher W. Good, et al.. (2020). A Machine Learning Approach to Management of Heart Failure Populations. JACC Heart Failure. 8(7). 578–587. 47 indexed citations

Raghunath, Sushravya, David P. vanMaanen, Joshua V. Stough, et al.. (2019). Abstract 14425: Deep Neural Networks Can Predict 1-Year Mortality Directly From ECG Signal, Even When Clinically Interpreted as Normal. Circulation. 2 indexed citations

Raghunath, Sushravya, Alvaro Ulloa, Dustin N. Hartzel, et al.. (2019). Deep neural networks can predict one-year mortality and incident atrial fibrillation from raw 12-lead electrocardiogram voltage data. Journal of Electrocardiology. 57. S104–S105. 2 indexed citations

Rajagopalan, Srinivasan, Sushravya Raghunath, Jennifer M. Boland, et al.. (2016). Computer-Aided Nodule Assessment and Risk Yield Risk Management of Adenocarcinoma: The Future of Imaging?. Seminars in Thoracic and Cardiovascular Surgery. 28(1). 120–126. 9 indexed citations

10.

Jacob, Joseph, Brian J. Bartholmai, Srinivasan Rajagopalan, et al.. (2016). Automated Quantitative Computed Tomography Versus Visual Computed Tomography Scoring in Idiopathic Pulmonary Fibrosis. Journal of Thoracic Imaging. 31(5). 304–311. 138 indexed citations

11.

Maldonado, Fabien, Fenghai Duan, Sushravya Raghunath, et al.. (2015). Noninvasive Computed Tomography–based Risk Stratification of Lung Adenocarcinomas in the National Lung Screening Trial. American Journal of Respiratory and Critical Care Medicine. 192(6). 737–744. 43 indexed citations

12.

Bartholmai, Brian J., Chi Wan Koo, Geoffrey B. Johnson, et al.. (2015). Pulmonary Nodule Characterization, Including Computer Analysis and Quantitative Features. Journal of Thoracic Imaging. 30(2). 139–156. 42 indexed citations

13.

Raghunath, Sushravya, Srinivasan Rajagopalan, Ronald A. Karwoski, et al.. (2014). Quantitative Stratification of Diffuse Parenchymal Lung Diseases. PLoS ONE. 9(3). e93229–e93229. 26 indexed citations

14.

Raghunath, Sushravya, Fabien Maldonado, Srinivasan Rajagopalan, et al.. (2014). Noninvasive Risk Stratification of Lung Adenocarcinoma using Quantitative Computed Tomography. Journal of Thoracic Oncology. 9(11). 1698–1703. 36 indexed citations

15.

Maldonado, Fabien, Jennifer M. Boland, Sushravya Raghunath, et al.. (2013). Noninvasive Characterization of the Histopathologic Features of Pulmonary Nodules of the Lung Adenocarcinoma Spectrum using Computer-Aided Nodule Assessment and Risk Yield (CANARY)—A Pilot Study. Journal of Thoracic Oncology. 8(4). 452–460. 53 indexed citations

16.

Bartholmai, Brian J., Sushravya Raghunath, Ronald A. Karwoski, et al.. (2013). Quantitative Computed Tomography Imaging of Interstitial Lung Diseases. Journal of Thoracic Imaging. 28(5). 298–307. 120 indexed citations

17.

Maldonado, Fabien, Teng Moua, Srinivasan Rajagopalan, et al.. (2013). Automated quantification of radiological patterns predicts survival in idiopathic pulmonary fibrosis. European Respiratory Journal. 43(1). 204–212. 173 indexed citations

18.

Maldonado, Fabien, Sushravya Raghunath, Marie Christine Aubry, et al.. (2012). Validation of CALIPER (Computer-aided lung informatics for pathology evaluation and rating) for the non-invasive assessment of pulmonary nodules of the adenocarcinoma spectrum. 40. 4184. 2 indexed citations

19.

Raghunath, Sushravya, Srinivasan Rajagopalan, Ronald A. Karwoski, Brian J. Bartholmai, & Richard A. Robb. (2012). Quantitative image analytics for stratified pulmonary medicine. 175. 1779–1782. 1 indexed citations

20.

Raghunath, Sushravya, Srinivasan Rajagopalan, Ronald A. Karwoski, Brian J. Bartholmai, & Richard A. Robb. (2011). Referenceless Stratification of Parenchymal Lung Abnormalities. Lecture notes in computer science. 14(Pt 3). 223–230. 3 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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