Ankush D. Jamthikar

1.5k total citations
35 papers, 754 citations indexed

About

Ankush D. Jamthikar is a scholar working on Cardiology and Cardiovascular Medicine, Pulmonary and Respiratory Medicine and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Ankush D. Jamthikar has authored 35 papers receiving a total of 754 indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Cardiology and Cardiovascular Medicine, 20 papers in Pulmonary and Respiratory Medicine and 17 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Ankush D. Jamthikar's work include Cardiovascular Health and Disease Prevention (20 papers), Cerebrovascular and Carotid Artery Diseases (19 papers) and Cardiac Imaging and Diagnostics (15 papers). Ankush D. Jamthikar is often cited by papers focused on Cardiovascular Health and Disease Prevention (20 papers), Cerebrovascular and Carotid Artery Diseases (19 papers) and Cardiac Imaging and Diagnostics (15 papers). Ankush D. Jamthikar collaborates with scholars based in United States, India and Italy. Ankush D. Jamthikar's co-authors include Deep Gupta, John R. Laird, Jasjit S. Suri, Narendra N. Khanna, Luca Saba, George D. Kitas, Sophie Mavrogeni, Andrew Nicolaides, Petros P. Sfikakis and Athanase D. Protogerou and has published in prestigious journals such as Journal of the American College of Cardiology, IEEE Transactions on Instrumentation and Measurement and JACC. Cardiovascular imaging.

In The Last Decade

Ankush D. Jamthikar

32 papers receiving 742 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ankush D. Jamthikar United States	20	374	304	278	112	111	35	754
Sukrit Narula United States	13	348 0.9×	68 0.2×	218 0.8×	68 0.6×	56 0.5×	19	829
Jeremy R. Burt United States	15	352 0.9×	216 0.7×	532 1.9×	17 0.2×	121 1.1×	69	952
Tomaž Omerzu Slovenia	7	158 0.4×	163 0.5×	204 0.7×	27 0.2×	80 0.7×	9	429
Christina Luong Canada	13	426 1.1×	209 0.7×	232 0.8×	38 0.3×	57 0.5×	55	801
Sumit K. Banchhor India	12	186 0.5×	219 0.7×	206 0.7×	33 0.3×	79 0.7×	19	395
Elizabeth Le United Kingdom	13	147 0.4×	225 0.7×	483 1.7×	14 0.1×	186 1.7×	24	826
Sravani Gajjala United States	3	329 0.9×	81 0.3×	284 1.0×	38 0.3×	58 0.5×	7	575
Sirish Shrestha United States	11	414 1.1×	76 0.3×	414 1.5×	86 0.8×	87 0.8×	26	810
Márton Tokodi Hungary	22	812 2.2×	349 1.1×	233 0.8×	25 0.2×	29 0.3×	73	1.0k
Alaa Mabrouk Salem Omar United States	11	844 2.3×	91 0.3×	610 2.2×	81 0.7×	64 0.6×	55	1.2k

Countries citing papers authored by Ankush D. Jamthikar

Since Specialization

Citations

This map shows the geographic impact of Ankush D. Jamthikar'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 Ankush D. Jamthikar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ankush D. Jamthikar more than expected).

Fields of papers citing papers by Ankush D. Jamthikar

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ankush D. Jamthikar. 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 Ankush D. Jamthikar. The network helps show where Ankush D. Jamthikar may publish in the future.

Co-authorship network of co-authors of Ankush D. Jamthikar

This figure shows the co-authorship network connecting the top 25 collaborators of Ankush D. Jamthikar. A scholar is included among the top collaborators of Ankush D. Jamthikar 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 Ankush D. Jamthikar. Ankush D. Jamthikar 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

Kagiyama, Nobuyuki, Márton Tokodi, Quincy A. Hathaway, et al.. (2025). PRIME 2.0: Proposed Requirements for Cardiovascular Imaging-Related Multimodal-AI Evaluation. JACC. Cardiovascular imaging. 19(2). 225–251. 1 indexed citations

Radhakrishnan, Aditya, Naveena Yanamala, Ankush D. Jamthikar, et al.. (2025). Synthetic generation of cardiac tissue motion from surface electrocardiograms. Nature Cardiovascular Research. 4(4). 445–457.

Jamthikar, Ankush D., Quincy A. Hathaway, Kameswari Maganti, et al.. (2025). Ultrasonic Texture Analysis for Predicting Acute Myocardial Infarction. JACC. Cardiovascular imaging. 18(11). 1185–1199. 1 indexed citations

Jamthikar, Ankush D., Rohan Shah, Márton Tokodi, Partho P. Sengupta, & Naveena Yanamala. (2024). Dissecting the latent representation of age inside a deep neural network’s predictions of diastolic dysfunction using echocardiographic variables. Biomedical Signal Processing and Control. 92. 106013–106013. 1 indexed citations

Hathaway, Quincy A., Ankush D. Jamthikar, Bernard Chaitman, et al.. (2024). Cardiac ultrasomics for acute myocardial infarction risk stratification and prediction of all-cause mortality: a feasibility study. Echo Research and Practice. 11(1). 22–22. 5 indexed citations

Tokodi, Márton, Rohan Shah, Ankush D. Jamthikar, et al.. (2024). Deep Learning Model of Diastolic Dysfunction Risk Stratifies the Progression of Early-Stage Aortic Stenosis. JACC. Cardiovascular imaging. 18(2). 150–165. 2 indexed citations

Konstantonis, George, Krishna V. Singh, Petros P. Sfikakis, et al.. (2022). Cardiovascular disease detection using machine learning and carotid/femoral arterial imaging frameworks in rheumatoid arthritis patients. Rheumatology International. 42(2). 215–239. 27 indexed citations

Jamthikar, Ankush D., Deep Gupta, Laura E. Mantella, et al.. (2021). Ensemble Machine Learning and Its Validation for Prediction of Coronary Artery Disease and Acute Coronary Syndrome Using Focused Carotid Ultrasound. IEEE Transactions on Instrumentation and Measurement. 71. 1–10. 20 indexed citations

Johri, Amer M., Laura E. Mantella, Ankush D. Jamthikar, et al.. (2021). Role of artificial intelligence in cardiovascular risk prediction and outcomes: comparison of machine-learning and conventional statistical approaches for the analysis of carotid ultrasound features and intra-plaque neovascularization. International journal of cardiac imaging. 37(11). 3145–3156. 20 indexed citations

10.

Viswanathan, Vijay, Anudeep Puvvula, Ankush D. Jamthikar, et al.. (2021). Bidirectional link between diabetes mellitus and coronavirus disease 2019 leading to cardiovascular disease: A narrative review. World Journal of Diabetes. 12(3). 215–237. 24 indexed citations

11.

Puvvula, Anudeep, Ankush D. Jamthikar, Deep Gupta, et al.. (2020). Morphological Carotid Plaque Area Is Associated With Glomerular Filtration Rate: A Study of South Asian Indian Patients With Diabetes and Chronic Kidney Disease. Angiology. 71(6). 520–535. 20 indexed citations

12.

Viswanathan, Vijay, Ankush D. Jamthikar, Deep Gupta, et al.. (2020). Does the Carotid Bulb Offer a Better 10-Year CVD/Stroke Risk Assessment Compared to the Common Carotid Artery? A 1516 Ultrasound Scan Study. Angiology. 71(10). 920–933. 13 indexed citations

13.

Jamthikar, Ankush D., Deep Gupta, Anudeep Puvvula, et al.. (2020). Cardiovascular risk assessment in patients with rheumatoid arthritis using carotid ultrasound B-mode imaging. Rheumatology International. 40(12). 1921–1939. 29 indexed citations

14.

Jamthikar, Ankush D., Deep Gupta, Elisa Cuadrado‐Godia, et al.. (2020). Ultrasound-based stroke/cardiovascular risk stratification using Framingham Risk Score and ASCVD Risk Score based on “Integrated Vascular Age” instead of “Chronological Age”: a multi-ethnic study of Asian Indian, Caucasian, and Japanese cohorts. Cardiovascular Diagnosis and Therapy. 10(4). 939–954. 15 indexed citations

15.

Jamthikar, Ankush D., Deep Gupta, Luca Saba, et al.. (2020). Cardiovascular/stroke risk predictive calculators: a comparison between statistical and machine learning models. Cardiovascular Diagnosis and Therapy. 10(4). 919–938. 39 indexed citations

16.

Jamthikar, Ankush D., Deep Gupta, Amer M. Johri, et al.. (2020). Low-Cost Office-Based Cardiovascular Risk Stratification Using Machine Learning and Focused Carotid Ultrasound in an Asian-Indian Cohort. Journal of Medical Systems. 44(12). 208–208. 16 indexed citations

17.

Jamthikar, Ankush D., Deep Gupta, Luca Saba, et al.. (2020). Artificial intelligence framework for predictive cardiovascular and stroke risk assessment models: A narrative review of integrated approaches using carotid ultrasound. Computers in Biology and Medicine. 126. 104043–104043. 35 indexed citations

18.

Khanna, Narendra N., Ankush D. Jamthikar, Tadashi Araki, et al.. (2019). Nonlinear model for the carotid artery disease 10‐year risk prediction by fusing conventional cardiovascular factors to carotid ultrasound image phenotypes: A Japanese diabetes cohort study. Echocardiography. 36(2). 345–361. 30 indexed citations

19.

Jamthikar, Ankush D., Deep Gupta, Narendra N. Khanna, et al.. (2019). A low-cost machine learning-based cardiovascular/stroke risk assessment system: integration of conventional factors with image phenotypes. Cardiovascular Diagnosis and Therapy. 9(5). 420–430. 57 indexed citations

20.

Kotsis, Vasilios, Ankush D. Jamthikar, Tadashi Araki, et al.. (2018). Echolucency-based phenotype in carotid atherosclerosis disease for risk stratification of diabetes patients. Diabetes Research and Clinical Practice. 143. 322–331. 22 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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