Bhargava Chinni

522 total citations
38 papers, 384 citations indexed

About

Bhargava Chinni is a scholar working on Biomedical Engineering, Mechanics of Materials and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Bhargava Chinni has authored 38 papers receiving a total of 384 indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Biomedical Engineering, 20 papers in Mechanics of Materials and 18 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Bhargava Chinni's work include Photoacoustic and Ultrasonic Imaging (29 papers), Thermography and Photoacoustic Techniques (20 papers) and Infrared Thermography in Medicine (10 papers). Bhargava Chinni is often cited by papers focused on Photoacoustic and Ultrasonic Imaging (29 papers), Thermography and Photoacoustic Techniques (20 papers) and Infrared Thermography in Medicine (10 papers). Bhargava Chinni collaborates with scholars based in United States, India and Belgium. Bhargava Chinni's co-authors include Navalgund Rao, Vikram S. Dogra, K. Valluru, Shweta Bhatt, Jacob Moalem, Ellen Giampoli, Jorge L. Yao, Ahmed Ghazi, Edward M. Messing and Jean Joseph and has published in prestigious journals such as Circulation, SHILAP Revista de lepidopterología and American Journal of Roentgenology.

In The Last Decade

Bhargava Chinni

34 papers receiving 373 citations

Peers

Bhargava Chinni

RNMI

BIOPH

PRM

Derek Allman United States

Hailong He Germany

Elena Merčep Germany

Michelle Heijblom Netherlands

Yunhao Zhu China

Nikolina‐Alexia Fasoula Germany

Alexander Dima Germany

Lei Yao United States

Arthur Chavignon France

Janek Gröhl Germany

Derek Allman United States

Bhargava Chinni

325 ×0.9

160 ×0.8

187 ×1.0

RNMI

15 ×0.7

BIOPH

10 ×0.5

PRM

Citations per year, relative to Bhargava Chinni Bhargava Chinni (= 1×) peers Derek Allman

Countries citing papers authored by Bhargava Chinni

Since Specialization

Citations

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

Fields of papers citing papers by Bhargava Chinni

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Bhargava Chinni. 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 Bhargava Chinni. The network helps show where Bhargava Chinni may publish in the future.

Co-authorship network of co-authors of Bhargava Chinni

This figure shows the co-authorship network connecting the top 25 collaborators of Bhargava Chinni. A scholar is included among the top collaborators of Bhargava Chinni 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 Bhargava Chinni. Bhargava Chinni 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

Nield, Lynne E., Cedric Manlhiot, Lindsay R. Freud, et al.. (2024). Machine Learning to Predict Outcomes of Fetal Cardiac Disease: A Pilot Study. Pediatric Cardiology. 46(4). 895–901.

Jacquemyn, Xander, et al.. (2024). Phenotypic clustering of repaired Tetralogy of Fallot using unsupervised machine learning. SHILAP Revista de lepidopterología. 17. 100524–100524. 1 indexed citations

Jacquemyn, Xander, et al.. (2024). Computational simulation of the potential improvement in clinical outcomes of cardiovascular diseases with the use of a personalized predictive medicine approach. Journal of the American Medical Informatics Association. 31(8). 1704–1713. 2 indexed citations

Jacquemyn, Xander, et al.. (2024). Unsupervised machine learning identifies distinct phenotypes in cardiac complications of pediatric patients treated with anthracyclines. Cardio-Oncology. 10(1). 74–74. 1 indexed citations

Chinni, Bhargava & Cedric Manlhiot. (2024). Emerging Analytical Approaches for Personalized Medicine Using Machine Learning In Pediatric and Congenital Heart Disease. Canadian Journal of Cardiology. 40(10). 1880–1896. 4 indexed citations

Chinni, Bhargava, et al.. (2023). FGF21 and GDF15 are elevated in Barth Syndrome and are correlated to important clinical measures. Molecular Genetics and Metabolism. 140(3). 107676–107676.

Eynde, Jef Van den, et al.. (2023). Data Quality Degradation on Prediction Models Generated From Continuous Activity and Heart Rate Monitoring: Exploratory Analysis Using Simulation. JMIR Cardio. 7. e40524–e40524. 1 indexed citations

Eynde, Jef Van den, Bhargava Chinni, Hilary J. Vernon, et al.. (2023). Identifying responders to elamipretide in Barth syndrome: Hierarchical clustering for time series data. Orphanet Journal of Rare Diseases. 18(1). 76–76. 1 indexed citations

Chinni, Bhargava, et al.. (2019). Automatic cancer tissue detection using multispectral photoacoustic imaging. International Journal of Computer Assisted Radiology and Surgery. 15(2). 309–320. 16 indexed citations

10.

Chinni, Bhargava, et al.. (2019). Multiview spatial compounding using lens-based photoacoustic imaging system. Photoacoustics. 13. 85–94. 11 indexed citations

11.

Chinni, Bhargava, et al.. (2019). Transfer learning for automatic cancer tissue detection using multispectral photoacoustic imaging. 14. 140–140. 9 indexed citations

12.

Chinni, Bhargava, et al.. (2019). Feasibility of quantitative tissue characterization using novel parameters extracted from photoacoustic power spectrum. Biomedical Signal Processing and Control. 57. 101719–101719. 3 indexed citations

13.

Chinni, Bhargava, et al.. (2018). Two-sided residual refocusing for an acoustic lens-based photoacoustic imaging system. Physics in Medicine and Biology. 63(13). 13NT03–13NT03. 6 indexed citations

14.

Chinni, Bhargava, et al.. (2018). Innovative Approach For Including Dual Mode Ultrasound And Volumetric Imaging Capability Within A Medical Photoacoustic Imaging Camera System. OW4D.2–OW4D.2. 2 indexed citations

15.

Chinni, Bhargava, et al.. (2017). Photoacoustic simulation study of chirp excitation response from different size absorbers. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 10139. 101391L–101391L. 4 indexed citations

16.

Rao, Navalgund, et al.. (2016). Evaluation of Frequency Domain Analysis of a Multiwavelength Photoacoustic Signal for Differentiating Malignant From Benign and Normal Prostates. Journal of Ultrasound in Medicine. 35(10). 2165–2177. 21 indexed citations

17.

Dogra, Vikram S., Bhargava Chinni, Shalini Singh, et al.. (2016). Photoacoustic imaging with an acoustic lens detects prostate cancer cells labeled with PSMA-targeting near-infrared dye-conjugates. Journal of Biomedical Optics. 21(6). 66019–66019. 14 indexed citations

18.

Chinni, Bhargava, Vikram S. Dogra, Navalgund Rao, et al.. (2015). Fabrication of a novel C-scan photoacoustic imaging camera. 1–4. 1 indexed citations

19.

Rao, Navalgund, et al.. (2014). Frequency analysis of multispectral photoacoustic images for differentiating malignant region from normal region in excised human prostate. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 9040. 90400P–90400P. 4 indexed citations

20.

Rao, Navalgund, et al.. (2008). Acoustic lens characterization for ultrasound and photoacoustic C-scan imaging modalities. PubMed. 2008. 2177–2180. 10 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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