Travis K. Redd

986 total citations
41 papers, 598 citations indexed

About

Travis K. Redd is a scholar working on Ophthalmology, Radiology, Nuclear Medicine and Imaging and Health Information Management. According to data from OpenAlex, Travis K. Redd has authored 41 papers receiving a total of 598 indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Ophthalmology, 12 papers in Radiology, Nuclear Medicine and Imaging and 5 papers in Health Information Management. Recurrent topics in Travis K. Redd's work include Ocular Infections and Treatments (14 papers), Retinal and Optic Conditions (9 papers) and Retinal Diseases and Treatments (7 papers). Travis K. Redd is often cited by papers focused on Ocular Infections and Treatments (14 papers), Retinal and Optic Conditions (9 papers) and Retinal Diseases and Treatments (7 papers). Travis K. Redd collaborates with scholars based in United States, India and Australia. Travis K. Redd's co-authors include J. Peter Campbell, Michael F. Chiang, Gerami D. Seitzman, Susan Ostmo, Jayashree Kalpathy–Cramer, James M. Brown, Thomas M. Lietman, R.V. Paul Chan, Akshay S. Thomas and Robison Vernon Paul Chan and has published in prestigious journals such as JAMA, SHILAP Revista de lepidopterología and PLoS ONE.

In The Last Decade

Travis K. Redd

36 papers receiving 587 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Travis K. Redd United States 14 305 253 96 91 80 41 598
David W. Parke United States 15 463 1.5× 1.1k 4.4× 25 0.3× 14 0.2× 110 1.4× 32 1.3k
Carlos Eduardo Leite Arieta Brazil 17 260 0.9× 468 1.8× 16 0.2× 40 0.4× 187 2.3× 69 822
Grace Hui‐Min Wu Taiwan 11 133 0.4× 95 0.4× 11 0.1× 30 0.3× 40 0.5× 21 549
Xiao-Shuang Zheng China 6 179 0.6× 26 0.1× 20 0.2× 125 1.4× 52 0.7× 8 1.4k
Yasín A. Khan United States 11 156 0.5× 163 0.6× 8 0.1× 72 0.8× 93 1.2× 24 476
Jane Barratt Switzerland 10 163 0.5× 193 0.8× 6 0.1× 8 0.1× 15 0.2× 20 478
Margaret Chang United States 11 413 1.4× 507 2.0× 11 0.1× 10 0.1× 79 1.0× 24 904
Kevin Forde Australia 8 161 0.5× 362 1.4× 2 0.0× 14 0.2× 300 3.8× 10 577
Christopher Gilpin Switzerland 18 41 0.1× 25 0.1× 18 0.2× 42 0.5× 30 0.4× 30 622
Ada Adriano United Kingdom 5 92 0.3× 8 0.0× 13 0.1× 40 0.4× 22 0.3× 11 866

Countries citing papers authored by Travis K. Redd

Since Specialization
Citations

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

Fields of papers citing papers by Travis K. Redd

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Travis K. Redd. 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 Travis K. Redd. The network helps show where Travis K. Redd may publish in the future.

Co-authorship network of co-authors of Travis K. Redd

This figure shows the co-authorship network connecting the top 25 collaborators of Travis K. Redd. A scholar is included among the top collaborators of Travis K. Redd 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 Travis K. Redd. Travis K. Redd 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.
Niziol, Leslie M., Ziyun Yang, Yiqing Wang, et al.. (2025). Association of Deep Learning Imaging Algorithm Measures of Microbial Keratitis With Vision Outcomes. Cornea.
2.
Ahuja, Abhimanyu S., et al.. (2025). Applications of Computer Vision for Infectious Keratitis: A Systematic Review. Ophthalmology Science. 5(6). 100861–100861.
3.
Prajna, N. Venkatesh, Jennifer Rose‐Nussbaumer, Thomas M. Lietman, et al.. (2024). Multimodal Deep Learning for Differentiating Bacterial and Fungal Keratitis Using Prospective Representative Data. SHILAP Revista de lepidopterología. 5(2). 100665–100665. 3 indexed citations
4.
Lalitha, Prajna, Gunasekaran Rameshkumar, Thomas M. Lietman, et al.. (2024). Automated Detection of Filamentous Fungal Keratitis on Whole Slide Images of Potassium Hydroxide Smears with Multiple Instance Learning. Ophthalmology Science. 5(2). 100653–100653. 2 indexed citations
5.
Pimentel, Matthew A., et al.. (2023). Rubella virus‐associated necrotizing granulomatous inflammation with extensive eyelid, ocular, and orbital involvement. Pediatric Dermatology. 40(6). 1107–1111. 1 indexed citations
6.
Redd, Travis K., et al.. (2023). Conjunctival Lesions Secondary to Systemic Varicella Zoster Virus Infection. PubMed. 2(4). e0022–e0022.
7.
Redd, Travis K., et al.. (2022). Alternaria fungus growing on top of cyanoacrylate glue in a patient with perforated corneal ulcer. American Journal of Ophthalmology Case Reports. 28. 101717–101717. 2 indexed citations
8.
Redd, Travis K., et al.. (2022). Very late onset LASIK flap Acremonium fungal keratitis confirmed by metagenomic deep sequencing. American Journal of Ophthalmology Case Reports. 25. 101294–101294. 5 indexed citations
9.
Redd, Travis K., N. Venkatesh Prajna, Muthiah Srinivasan, et al.. (2022). Image-Based Differentiation of Bacterial and Fungal Keratitis Using Deep Convolutional Neural Networks. SHILAP Revista de lepidopterología. 2(2). 100119–100119. 40 indexed citations
10.
Redd, Travis K., Luca Della Santina, N. Venkatesh Prajna, et al.. (2021). Automated Differentiation of Bacterial from Fungal Keratitis Using Deep Learning. Investigative Ophthalmology & Visual Science. 62(8). 2161–2161. 2 indexed citations
11.
Campbell, J. Peter, Praveer Singh, Travis K. Redd, et al.. (2021). Applications of Artificial Intelligence for Retinopathy of Prematurity Screening. PEDIATRICS. 147(3). e2020016618–e2020016618. 57 indexed citations
12.
Krishnan, Tiruvengada, et al.. (2020). The Prognostic Value of Persistent Culture Positivity in Fungal Keratitis in the Mycotic Antimicrobial Localized Injection Trial. American Journal of Ophthalmology. 215. 1–7. 3 indexed citations
13.
Pasricha, Neel D., Zeeshan Haq, Lawrence Chan, et al.. (2020). Remote corneal suturing wet lab: microsurgical education during the COVID-19 pandemic. Journal of Cataract & Refractive Surgery. 46(12). 1667–1673. 18 indexed citations
14.
Redd, Travis K., J. Peter Campbell, James M. Brown, et al.. (2018). Evaluation of a deep learning image assessment system for detecting severe retinopathy of prematurity. British Journal of Ophthalmology. 103(5). 580–584. 107 indexed citations
15.
Thomas, Akshay S., Travis K. Redd, & Thomas S. Hwang. (2016). Improving the Transition to Ophthalmology Residency: A Survey of First-Year Ophthalmology Residents. 8(1). e10–e18. 4 indexed citations
16.
Doberne, Julie, Travis K. Redd, Thomas R. Yackel, et al.. (2016). Perspectives and Uses of the Electronic Health Record Among US Pediatricians. Journal of Ambulatory Care Management. 40(1). 59–68. 7 indexed citations
17.
18.
Redd, Travis K., Akshay S. Thomas, & Thomas S. Hwang. (2014). Improving the Transition to Ophthalmology Residency: A Survey of First-Year Ophthalmology Residents. Investigative Ophthalmology & Visual Science. 55(13). 5578–5578. 1 indexed citations
19.
Redd, Travis K., et al.. (2013). Implementation of electronic health record systems in ophthalmology: impact on clinical volume compared to other medical fields. Investigative Ophthalmology & Visual Science. 54(15). 4426–4426.
20.
Redd, Travis K. & Herbert A. Thompson. (1995). Secretion of proteins by Coxiella burnetii. Microbiology. 141(2). 363–369. 15 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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