Ryo Ito

80 papers receiving 1.6k citations

Peers

Ryo Ito
Comparison fields: 5 of 145
  • Endocrinology, Diabetes and Metabolism 415
  • Developmental Biology 37
  • Computer Vision and Pattern Recognition 208
  • Surgery 422
  • Molecular Biology 566
Replace Michael J. Black with:
Michael J. Black United States
Matthew J. Callow United States
Sung Hak Lee South Korea
Kiyoshi Arai Japan
Jianshuang Li China
Xiaolong Li China
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Ryo Ito relative to Michael J. Black United States Michael J. Black's profile →
Citations per field
00.5×12.2×
Michael J. Black · 1×
Citations per year

Countries citing papers authored by Ryo Ito

Since Specialization
Citations

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

Fields of papers citing papers by Ryo Ito

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Ryo Ito, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Ryo Ito Line = papers co-authored together Ryo Ito links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 90 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2010210
2
Image Size Invariant Visual Cryptography
1999175
3 2011125
4 2015109
5 201378
6 201268
7 201359
8 201958
9 200951
10 201444
11 198642
12 201139
13 201337
14 201333
15 201931
16 201727
17 202026
18 201225
19 202223
20 201822

About Ryo Ito

Ryo Ito is a scholar working on Surgery, Molecular Biology, Cardiology and Cardiovascular Medicine, Endocrinology, Diabetes and Metabolism and Genetics, having authored 90 papers that have together received 1.6k indexed citations. Recurring topics across this work include Pancreatic function and diabetes (20 papers), Cardiac Arrhythmias and Treatments (12 papers), Cardiac pacing and defibrillation studies (10 papers), Diabetes Treatment and Management (9 papers), Pluripotent Stem Cells Research (8 papers), Atrial Fibrillation Management and Outcomes (5 papers), Cardiac electrophysiology and arrhythmias (5 papers) and Diabetes Management and Research (4 papers). The work is most often cited by research in Endocrinology, Diabetes and Metabolism (415 citations), Developmental Biology (37 citations), Computer Vision and Pattern Recognition (208 citations), Surgery (422 citations) and Molecular Biology (566 citations). Ryo Ito has collaborated with scholars based in Japan, United States and Mongolia. Frequent co-authors include Hatsukazu Tanaka, Hidenori Kuwakado, Yoshiyuki Tsujihata, Koji Takeuchi, Nobuyuki Negoro, Akira Mori, Masami Suzuki, Yū Momose, Tsuneo Yasuma and Ayako Harada. Their work appears in journals such as Scientific Reports, Journal of Cardiology, Proceedings of the National Academy of Sciences, Journal of Interventional Cardiac Electrophysiology and Journal of Pharmacology and Experimental Therapeutics.

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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