A. Takigami

703 total citations · 1 hit paper
7 papers, 411 citations indexed

About

A. Takigami is a scholar working on Cardiology and Cardiovascular Medicine, Radiology, Nuclear Medicine and Imaging and Surgery. According to data from OpenAlex, A. Takigami has authored 7 papers receiving a total of 411 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Cardiology and Cardiovascular Medicine, 4 papers in Radiology, Nuclear Medicine and Imaging and 2 papers in Surgery. Recurrent topics in A. Takigami's work include Cardiac Imaging and Diagnostics (3 papers), Acute Myocardial Infarction Research (3 papers) and EEG and Brain-Computer Interfaces (2 papers). A. Takigami is often cited by papers focused on Cardiac Imaging and Diagnostics (3 papers), Acute Myocardial Infarction Research (3 papers) and EEG and Brain-Computer Interfaces (2 papers). A. Takigami collaborates with scholars based in United States, Switzerland and Germany. A. Takigami's co-authors include Edgard Morya, Hannes Bleuler, Renan C. Moioli, Simone Gallo, Sanjay S. Joshi, Solaiman Shokur, Ana R. C. Donati, Miguel A. L. Nicolelis, Gordon Cheng and Fabrício Lima Brasil and has published in prestigious journals such as Journal of Clinical Oncology, Scientific Reports and Journal of cardiovascular computed tomography.

In The Last Decade

A. Takigami

6 papers receiving 397 citations

Hit Papers

Sybil: A Validated Deep Learning Model to Predict Future ... 2023 2026 2024 2025 2023 25 50 75 100

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
A. Takigami United States 3 177 137 98 90 82 7 411
Fanny Quandt Germany 10 240 1.4× 122 0.9× 115 1.2× 56 0.6× 88 1.1× 35 442
Kyle J. Edmunds Iceland 15 33 0.2× 181 1.3× 67 0.7× 60 0.7× 59 0.7× 30 517
Paymon G. Rezaii United States 13 305 1.7× 127 0.9× 188 1.9× 57 0.6× 7 0.1× 30 579
Cristina Daia Romania 9 120 0.7× 58 0.4× 94 1.0× 13 0.1× 37 0.5× 33 392
С. В. Котов Russia 10 290 1.6× 150 1.1× 144 1.5× 51 0.6× 180 2.2× 111 658
Matt C. Gabel United Kingdom 6 51 0.3× 144 1.1× 40 0.4× 142 1.6× 33 0.4× 8 485
Giacomo Donato Cascarano Italy 12 52 0.3× 121 0.9× 13 0.1× 90 1.0× 15 0.2× 19 428
Xing Qian Singapore 10 266 1.5× 29 0.2× 82 0.8× 71 0.8× 18 0.2× 26 430
Aleksandar Miladinović Italy 10 157 0.9× 38 0.3× 54 0.6× 45 0.5× 19 0.2× 42 330
Valérie Delvaux Belgium 15 156 0.9× 130 0.9× 45 0.5× 69 0.8× 173 2.1× 33 825

Countries citing papers authored by A. Takigami

Since Specialization
Citations

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

Fields of papers citing papers by A. Takigami

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of A. Takigami

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

All Works

7 of 7 papers shown
1.
Takigami, A., Vikas Thondapu, Éric Zhang, et al.. (2024). Selective Use of CT Fractional Flow at a Large Academic Medical Center: Insights from Clinical Implementation after 1 Year of Practice. Radiology Cardiothoracic Imaging. 6(2). e230073–e230073. 2 indexed citations
2.
Mikhael, Peter G., Jeremy Wohlwend, Adam Yala, et al.. (2023). Sybil: A Validated Deep Learning Model to Predict Future Lung Cancer Risk From a Single Low-Dose Chest Computed Tomography. Journal of Clinical Oncology. 41(12). 2191–2200. 114 indexed citations breakdown →
3.
Macruz, Fabíola, Charles Lu, A. Takigami, et al.. (2022). Quantification of the Thoracic Aorta and Detection of Aneurysm at CT: Development and Validation of a Fully Automatic Methodology. Radiology Artificial Intelligence. 4(2). e210076–e210076. 16 indexed citations
4.
Takigami, A., Vikas Thondapu, Reece Goiffon, et al.. (2021). Coronary Artery Disease Reporting and Data System (CAD-RADS) Adoption: Analysis of Local Trends in a Large Academic Medical Center. Radiology Cardiothoracic Imaging. 3(3). e210016–e210016. 1 indexed citations
5.
Goiffon, Reece, Vikas Thondapu, A. Takigami, et al.. (2020). Diagnostic Performance Of Coronary Ct Angiography Compared To Invasive Coronary Angiography In A Large Academic Practice. Journal of cardiovascular computed tomography. 14(3). S16–S17. 1 indexed citations
6.
Donati, Ana R. C., Solaiman Shokur, Edgard Morya, et al.. (2016). Long-Term Training with a Brain-Machine Interface-Based Gait Protocol Induces Partial Neurological Recovery in Paraplegic Patients. Scientific Reports. 6(1). 30383–30383. 277 indexed citations
7.
Schwarz, Diemo, Solaiman Shokur, Renan C. Moioli, et al.. (2014). The walk again project: Brain-controlled exoskeleton locomotion.

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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