James Requa

604 total citations
22 papers, 422 citations indexed

About

James Requa is a scholar working on Oncology, Radiology, Nuclear Medicine and Imaging and Pulmonary and Respiratory Medicine. According to data from OpenAlex, James Requa has authored 22 papers receiving a total of 422 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Oncology, 10 papers in Radiology, Nuclear Medicine and Imaging and 8 papers in Pulmonary and Respiratory Medicine. Recurrent topics in James Requa's work include Colorectal Cancer Screening and Detection (12 papers), Radiomics and Machine Learning in Medical Imaging (9 papers) and Gastric Cancer Management and Outcomes (5 papers). James Requa is often cited by papers focused on Colorectal Cancer Screening and Detection (12 papers), Radiomics and Machine Learning in Medical Imaging (9 papers) and Gastric Cancer Management and Outcomes (5 papers). James Requa collaborates with scholars based in United States, India and Canada. James Requa's co-authors include Andrew Ninh, Tyler Dao, Jason Samarasena, William E. Karnes, William E. Karnes, Elise Tran, Kenneth J. Chang, Rintaro Hashimoto, Rony Zachariah and Efren Rael and has published in prestigious journals such as Gastroenterology, The American Journal of Gastroenterology and Gastrointestinal Endoscopy.

In The Last Decade

James Requa

20 papers receiving 400 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
James Requa United States 7 276 208 149 137 81 22 422
Andrew Ninh United States 7 251 0.9× 195 0.9× 142 1.0× 129 0.9× 79 1.0× 23 401
Tyler Dao United States 7 235 0.9× 184 0.9× 148 1.0× 122 0.9× 70 0.9× 16 386
Florian Soudan Canada 4 361 1.3× 205 1.0× 96 0.6× 212 1.5× 124 1.5× 5 473
Takahide Shinagawa Japan 9 225 0.8× 168 0.8× 162 1.1× 82 0.6× 31 0.4× 33 415
João Afonso Portugal 14 277 1.0× 197 0.9× 212 1.4× 98 0.7× 56 0.7× 55 479
Tiago Ribeiro Portugal 15 285 1.0× 208 1.0× 219 1.5× 97 0.7× 55 0.7× 57 506
Ayako Nakada Japan 9 308 1.1× 238 1.1× 168 1.1× 83 0.6× 43 0.5× 13 530
Maarten R. Struyvenberg Netherlands 11 298 1.1× 473 2.3× 469 3.1× 175 1.3× 66 0.8× 30 727
Fanhua Ming China 5 157 0.6× 131 0.6× 95 0.6× 100 0.7× 63 0.8× 7 351
Katsuro Ichimasa Japan 16 561 2.0× 436 2.1× 283 1.9× 135 1.0× 54 0.7× 63 719

Countries citing papers authored by James Requa

Since Specialization
Citations

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

Fields of papers citing papers by James Requa

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of James Requa

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

All Works

20 of 20 papers shown
1.
Byrne, Michelle L., James Requa, Julián Panés, et al.. (2025). P0371 Building a Robust Artificial Intelligence Solution for Use in Ulcerative Colitis Clinical Trials. Journal of Crohn s and Colitis. 19(Supplement_1). i852–i852. 1 indexed citations
2.
Requa, James, Rajni Mandal, Bonnie Balzer, et al.. (2022). High-fidelity detection, subtyping, and localization of five skin neoplasms using supervised and semi-supervised learning. Journal of Pathology Informatics. 14. 100159–100159. 7 indexed citations
3.
Lee, Ji Young, Audrey H. Calderwood, William E. Karnes, et al.. (2021). Artificial intelligence for the assessment of bowel preparation. Gastrointestinal Endoscopy. 95(3). 512–518.e1. 31 indexed citations
4.
5.
Samarasena, Jason, Vani J. Konda, Arvind J. Trindade, et al.. (2021). ID: 3522405 DETECTION OF EARLY ESOPHAGEAL NEOPLASIA IN BARRETT’S ESOPHAGUS USING REAL TIME ARTIFICIAL INTELLIGENCE: A MULTICENTER EXTERNAL VIDEO VALIDATION STUDY. Gastrointestinal Endoscopy. 93(6). AB195–AB195. 3 indexed citations
6.
Karnes, William E., et al.. (2021). S216 Automated Cecal Intubation Rate and Withdrawal Time With Artificial Intelligence. A Video Validation Study. The American Journal of Gastroenterology. 116(1). S96–S96. 2 indexed citations
7.
Gottlieb, Klaus, James Requa, William E. Karnes, et al.. (2020). Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks. Gastroenterology. 160(3). 710–719.e2. 92 indexed citations
8.
Hashimoto, Rintaro, James Requa, Tyler Dao, et al.. (2020). Artificial intelligence using convolutional neural networks for real-time detection of early esophageal neoplasia in Barrett’s esophagus (with video). Gastrointestinal Endoscopy. 91(6). 1264–1271.e1. 151 indexed citations
9.
Hashimoto, Rintaro, Elise Tran, Tyler Dao, et al.. (2019). 641 ARTIFICIAL INTELLIGENCE DYSPLASIA DETECTION (AIDD) ALGORITHM FOR BARRETT’S ESOPHAGUS. Gastrointestinal Endoscopy. 89(6). AB99–AB100. 3 indexed citations
10.
11.
Zachariah, Rony, Jason Samarasena, Tyler Dao, et al.. (2019). Prediction of Polyp Pathology Using Convolutional Neural Networks Achieves “Resect and Discard” Thresholds. The American Journal of Gastroenterology. 115(1). 138–144. 91 indexed citations
12.
Hashimoto, Rintaro, Nabil El Hage Chehade, Kenneth J. Chang, et al.. (2019). 384 High Accuracy and Effectiveness With Deep Neural Networks and Artificial Intelligence in Detection of Early Esophageal Neoplasia in Barrett's Esophagus. The American Journal of Gastroenterology. 114(1). S224–S225.
13.
Samarasena, Jason, David P. Lee, Tyler Dao, et al.. (2018). Artificial Intelligence Can Accurately Detect Tools Used During Colonoscopy: Another Step Forward Toward Autonomous Report Writing: Presidential Poster Award. The American Journal of Gastroenterology. 113(Supplement). S619–S620. 5 indexed citations
14.
Karnes, William E., Andrew Ninh, Tyler Dao, James Requa, & Jason Samarasena. (2018). Sa1925 REAL-TIME IDENTIFICATION OF ANATOMIC LANDMARKS DURING COLONOSCOPY USING DEEP LEARNING. Gastrointestinal Endoscopy. 87(6). AB252–AB252. 4 indexed citations
15.
Karnes, William E., et al.. (2018). Automated Documentation of Multiple Colonoscopy Quality Measures in Real-Time with Convolutional Neural Networks. The American Journal of Gastroenterology. 113(Supplement). S1532–S1532. 3 indexed citations
16.
Karnes, William E., Andrew Ninh, Tyler Dao, James Requa, & Jason Samarasena. (2018). Sa1940 UNAMBIGUOUS REAL-TIME SCORING OF BOWEL PREPARATION USING ARTIFICIAL INTELLIGENCE. Gastrointestinal Endoscopy. 87(6). AB258–AB258. 7 indexed citations
17.
Requa, James, Tyler Dao, Andrew Ninh, & William E. Karnes. (2018). Can a Convolutional Neural Network Solve the Polyp Size Dilemma? Category Award (Colorectal Cancer Prevention) Presidential Poster Award. The American Journal of Gastroenterology. 113(Supplement). S158–S158. 6 indexed citations
18.
Abadir, Alexander, James Requa, Andrew Ninh, William E. Karnes, & Mark Mattar. (2018). Unambiguous Real-Time Endoscopic Scoring of Ulcerative Colitis Using a Convolutional Neural Network. The American Journal of Gastroenterology. 113(Supplement). S349–S349. 3 indexed citations
19.
Zachariah, Rony, Andrew Ninh, Tyler Dao, James Requa, & William E. Karnes. (2018). Can Artificial Intelligence (AI) Achieve Real-Time ‘Resect and Discard‘ Thresholds Independently of Device or Operator?. The American Journal of Gastroenterology. 113(Supplement). S129–S129. 2 indexed citations
20.
Requa, James, et al.. (1971). PRELIMINARY USER'S MANUAL FOR THE STAR SYSTEM SOFTWARE.. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information).

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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