Akshayaa Vaidyanathan

818 total citations · 1 hit paper
18 papers, 472 citations indexed

About

Akshayaa Vaidyanathan is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Biomedical Engineering. According to data from OpenAlex, Akshayaa Vaidyanathan has authored 18 papers receiving a total of 472 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Radiology, Nuclear Medicine and Imaging, 6 papers in Pulmonary and Respiratory Medicine and 6 papers in Biomedical Engineering. Recurrent topics in Akshayaa Vaidyanathan's work include Radiomics and Machine Learning in Medical Imaging (9 papers), Advanced X-ray and CT Imaging (5 papers) and Lung Cancer Diagnosis and Treatment (3 papers). Akshayaa Vaidyanathan is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (9 papers), Advanced X-ray and CT Imaging (5 papers) and Lung Cancer Diagnosis and Treatment (3 papers). Akshayaa Vaidyanathan collaborates with scholars based in Netherlands, Belgium and United States. Akshayaa Vaidyanathan's co-authors include Philippe Lambin, Seán Walsh, Ralph T. H. Leijenaar, Fadila Zerka, Wim Vos, Benjamin Miraglio, Pierre Lovinfosse, Roland Hustinx, Julien Guiot and Fabio Bottari and has published in prestigious journals such as Journal of Clinical Oncology, Journal of Neurophysiology and Scientific Reports.

In The Last Decade

Akshayaa Vaidyanathan

16 papers receiving 466 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

A review in radiomics: Making personalized medicine a reality via routine imaging

2021 192 citations Julien Guiot, Akshayaa Vaidyanathan et al. Medicinal Research Reviews profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Akshayaa Vaidyanathan Netherlands	10	268	111	84	79	51	18	472
Benjamin Miraglio Netherlands	4	203 0.8×	81 0.7×	60 0.7×	104 1.3×	56 1.1×	7	380
Fadila Zerka Netherlands	7	418 1.6×	155 1.4×	124 1.5×	151 1.9×	70 1.4×	14	646
Morgan P. McBee United States	8	258 1.0×	108 1.0×	85 1.0×	99 1.3×	90 1.8×	16	492
Avi Ben-Cohen Israel	8	262 1.0×	68 0.6×	118 1.4×	209 2.6×	79 1.5×	10	594
Alanna Vial Australia	5	242 0.9×	62 0.6×	87 1.0×	117 1.5×	84 1.6×	8	440
Siri Willems Belgium	11	342 1.3×	97 0.9×	107 1.3×	158 2.0×	75 1.5×	14	616
Paul Desbordes France	9	285 1.1×	104 0.9×	71 0.8×	142 1.8×	57 1.1×	13	534
David Clunie United States	19	351 1.3×	209 1.9×	104 1.2×	244 3.1×	50 1.0×	44	838
Teresa Perillo Italy	12	239 0.9×	67 0.6×	46 0.5×	85 1.1×	50 1.0×	45	531

Countries citing papers authored by Akshayaa Vaidyanathan

Since Specialization

Citations

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

Fields of papers citing papers by Akshayaa Vaidyanathan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Akshayaa Vaidyanathan

This figure shows the co-authorship network connecting the top 25 collaborators of Akshayaa Vaidyanathan. A scholar is included among the top collaborators of Akshayaa Vaidyanathan 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 Akshayaa Vaidyanathan. Akshayaa Vaidyanathan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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18 of 18 papers shown

Parise, Orlando, Akshayaa Vaidyanathan, Mariaelena Occhipinti, et al.. (2023). Machine Learning to Identify Patients at Risk of Developing New-Onset Atrial Fibrillation after Coronary Artery Bypass. Journal of Cardiovascular Development and Disease. 10(2). 82–82. 10 indexed citations

Ibrahim, Abdalla, Akshayaa Vaidyanathan, Sergey Primakov, et al.. (2023). Deep learning based identification of bone scintigraphies containing metastatic bone disease foci. Cancer Imaging. 23(1). 12–12. 11 indexed citations

Leijenaar, Ralph T. H., Seán Walsh, Akshayaa Vaidyanathan, et al.. (2023). External validation of a radiomic signature to predict p16 (HPV) status from standard CT images of anal cancer patients. Scientific Reports. 13(1). 7198–7198. 6 indexed citations

Vaidyanathan, Akshayaa, Julien Guiot, Fadila Zerka, et al.. (2022). An externally validated fully automated deep learning algorithm to classify COVID-19 and other pneumonias on chest computed tomography. ERJ Open Research. 8(2). 579–2021. 6 indexed citations

Walsh, Seán, et al.. (2022). Nodule vascularity as novel radiomics imaging endpoint for lung cancer diagnosis and prognosis.. Journal of Clinical Oncology. 40(16_suppl). e20580–e20580.

Guiot, Julien, Akshayaa Vaidyanathan, Louis Deprez, et al.. (2021). A review in radiomics: Making personalized medicine a reality via routine imaging. Medicinal Research Reviews. 42(1). 426–440. 192 indexed citations breakdown →

Zerka, Fadila, Visara Urovi, Fabio Bottari, et al.. (2021). Privacy preserving distributed learning classifiers – Sequential learning with small sets of data. Computers in Biology and Medicine. 136. 104716–104716. 15 indexed citations

Vaidyanathan, Akshayaa, Ralph T. H. Leijenaar, Marc van Hoof, et al.. (2021). Deep learning for the fully automated segmentation of the inner ear on MRI. Scientific Reports. 11(1). 2885–2885. 44 indexed citations

Vaidyanathan, Akshayaa, Marjolein de Wit, Alida A. Postma, et al.. (2021). A non-invasive, automated diagnosis of Menière’s disease using radiomics and machine learning on conventional magnetic resonance imaging: A multicentric, case-controlled feasibility study. La radiologia medica. 127(1). 72–82. 26 indexed citations

10.

Frix, Anne-Noëlle, François Cousin, Turkey Refaee, et al.. (2021). Radiomics in Lung Diseases Imaging: State-of-the-Art for Clinicians. Journal of Personalized Medicine. 11(7). 602–602. 59 indexed citations

11.

Vaidyanathan, Akshayaa, Vincent Van Rompaey, Alida A. Postma, et al.. (2020). The “hype” of hydrops in classifying vestibular disorders: a narrative review. Journal of Neurology. 267(S1). 197–211. 22 indexed citations

12.

Zerka, Fadila, Akshayaa Vaidyanathan, Julien Guiot, et al.. (2020). Late Breaking Abstract - Development and validation of an automated radiomic CT signature for detecting?COVID-19. 4152–4152. 1 indexed citations

13.

Leijenaar, Ralph T. H., Fadila Zerka, Akshayaa Vaidyanathan, et al.. (2020). 5MO Prospective validation of a radiomics signature for chemoradiotherapy lung cancer patients. Annals of Oncology. 31. S246–S246. 1 indexed citations

14.

Leijenaar, Ralph T. H., et al.. (2020). OC-0587: Prospective Validation of a Radiomics Signature for Chemoradiotherapy Lung Cancer Patients. Radiotherapy and Oncology. 152. S330–S331. 1 indexed citations

15.

Zerka, Fadila, Visara Urovi, Akshayaa Vaidyanathan, et al.. (2020). Blockchain for Privacy Preserving and Trustworthy Distributed Machine Learning in Multicentric Medical Imaging (C-DistriM). IEEE Access. 8. 183939–183951. 61 indexed citations

16.

Meakin, James, Bart Liefers, Cristina González-Gonzalo, et al.. (2019). EyeNED workstation: Development of a multi-modal vendor-independent application for annotation, spatial alignment and analysis of retinal images. Investigative Ophthalmology & Visual Science. 60(9). 6118–6118. 4 indexed citations

17.

Vaidyanathan, Akshayaa, et al.. (2002). Adaptive image analysis for object recognition. I. Entropic object location. 2. 1888–1891.

18.

Rogers, Robert F., et al.. (2001). Information Theoretic Analysis of Pulmonary Stretch Receptor Spike Trains. Journal of Neurophysiology. 85(1). 448–461. 13 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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