Robert T. Chang

6.9k total citations · 3 hit papers
107 papers, 4.8k citations indexed

About

Robert T. Chang is a scholar working on Ophthalmology, Radiology, Nuclear Medicine and Imaging and Epidemiology. According to data from OpenAlex, Robert T. Chang has authored 107 papers receiving a total of 4.8k indexed citations (citations by other indexed papers that have themselves been cited), including 82 papers in Ophthalmology, 68 papers in Radiology, Nuclear Medicine and Imaging and 12 papers in Epidemiology. Recurrent topics in Robert T. Chang's work include Glaucoma and retinal disorders (60 papers), Retinal Imaging and Analysis (40 papers) and Retinal Diseases and Treatments (39 papers). Robert T. Chang is often cited by papers focused on Glaucoma and retinal disorders (60 papers), Retinal Imaging and Analysis (40 papers) and Retinal Diseases and Treatments (39 papers). Robert T. Chang collaborates with scholars based in United States, China and Hong Kong. Robert T. Chang's co-authors include Donald L. Budenz, William J. Feuer, Jean-Claude Mwanza, Andrés Velasco, O’Rese J. Knight, Kuldev Singh, Mingguang He, Stuart Keel, Zhixi Li and Wei Meng and has published in prestigious journals such as Cell, Blood and The Quarterly Journal of Economics.

In The Last Decade

Robert T. Chang

101 papers receiving 4.6k citations

Hit Papers

Efficacy of a Deep Learning System for Detecti... 2011 2026 2016 2021 2018 2011 2012 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Robert T. Chang United States 34 3.6k 3.0k 753 275 260 107 4.8k
Robert P. Finger Germany 44 4.3k 1.2× 3.0k 1.0× 125 0.2× 261 0.9× 85 0.3× 264 6.4k
Steven T. Bailey United States 33 5.1k 1.4× 4.3k 1.4× 1.2k 1.7× 93 0.3× 8 0.0× 114 6.1k
John B. Miller United States 30 2.2k 0.6× 1.7k 0.6× 244 0.3× 105 0.4× 26 0.1× 211 3.5k
Anat Loewenstein Israel 44 6.9k 1.9× 5.1k 1.7× 179 0.2× 453 1.6× 7 0.0× 248 8.1k
Paul B. Greenberg United States 21 1.6k 0.4× 949 0.3× 117 0.2× 271 1.0× 9 0.0× 146 2.8k
Xiaohang Wu China 22 902 0.2× 818 0.3× 124 0.2× 183 0.7× 5 0.0× 76 1.9k
Catherine Egan United Kingdom 45 4.3k 1.2× 3.5k 1.2× 311 0.4× 157 0.6× 2 0.0× 183 5.7k
Maria Vittoria Cicinelli Italy 32 2.4k 0.7× 1.7k 0.6× 149 0.2× 190 0.7× 12 0.0× 198 3.3k
Yousif Subhi Denmark 28 1.6k 0.4× 970 0.3× 175 0.2× 258 0.9× 2 0.0× 161 2.6k
Nicole Eter Germany 43 4.9k 1.3× 3.7k 1.2× 379 0.5× 246 0.9× 1 0.0× 354 6.6k

Countries citing papers authored by Robert T. Chang

Since Specialization
Citations

This map shows the geographic impact of Robert T. Chang'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 Robert T. Chang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Robert T. Chang more than expected).

Fields of papers citing papers by Robert T. Chang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Robert T. Chang. 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 Robert T. Chang. The network helps show where Robert T. Chang may publish in the future.

Co-authorship network of co-authors of Robert T. Chang

This figure shows the co-authorship network connecting the top 25 collaborators of Robert T. Chang. A scholar is included among the top collaborators of Robert T. Chang 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 Robert T. Chang. Robert T. Chang 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.
Zhang, Y, et al.. (2025). Multimodal Artificial Intelligence Models Predicting Glaucoma Progression Using Electronic Health Records and Retinal Nerve Fiber Layer Scans. Translational Vision Science & Technology. 14(3). 27–27. 3 indexed citations
2.
Shue, Ann, et al.. (2024). Glaucoma Home Self-Testing Using VR Visual Fields and Rebound Tonometry Versus In-Clinic Perimetry and Goldmann Applanation Tonometry: A Pilot Study. Translational Vision Science & Technology. 13(8). 7–7. 6 indexed citations
3.
Yang, Zefeng, Biao Wang, Fengqi Zhou, et al.. (2024). Understanding natural language: Potential application of large language models to ophthalmology. Asia-Pacific Journal of Ophthalmology. 13(4). 100085–100085. 18 indexed citations
5.
Wolf, Julian, Karen M. Wai, Robert T. Chang, et al.. (2023). Biobanking of Human Aqueous and Vitreous Liquid Biopsies for Molecular Analyses. Journal of Visualized Experiments. 3 indexed citations
6.
Sun, Michelle T., et al.. (2023). Glaucoma and Myopia: Diagnostic Challenges. Biomolecules. 13(3). 562–562. 19 indexed citations
7.
Gui, Haiwen, Y Zhang, Robert T. Chang, & Sophia Y. Wang. (2023). Real-world agreement of same-visit Tono-Pen vs Goldmann applanation intraocular pressure measurements using electronic health records. Heliyon. 9(8). e18703–e18703. 2 indexed citations
8.
Stell, Laurel, Muhammad Sohail Halim, Sylvia L. Groth, et al.. (2021). Phase 1b Randomized Controlled Study of Short Course Topical Recombinant Human Nerve Growth Factor (rhNGF) for Neuroenhancement in Glaucoma: Safety, Tolerability, and Efficacy Measure Outcomes. American Journal of Ophthalmology. 234. 223–234. 22 indexed citations
9.
Tran, Elaine, et al.. (2020). Comparison of Virtual Reality (PalmScan VF2000) Visual Fields Analyzer with Humphrey Visual Field in Glaucoma Patients. Investigative Ophthalmology & Visual Science. 61(7). 3893–3893. 1 indexed citations
10.
Ran, An Ran, Xi Wang, Luyang Luo, et al.. (2019). A 3D Deep Learning System for Detecting Glaucomatous Optic Neuropathy from Volumetric and En Face Optical Coherence Tomography Scans. Investigative Ophthalmology & Visual Science. 60(9). 5571–5571. 1 indexed citations
11.
Keel, Stuart, Zhixi Li, Jane Scheetz, et al.. (2019). Development and validation of a deep‐learning algorithm for the detection of neovascular age‐related macular degeneration from colour fundus photographs. Clinical and Experimental Ophthalmology. 47(8). 1009–1018. 60 indexed citations
12.
Groth, Sylvia L., et al.. (2019). Recombinant human nerve growth factor (rhNGF) eye drops for glaucoma: Results from a prospective double -masked randomized controlled trial. Investigative Ophthalmology & Visual Science. 60(9). 2397–2397. 1 indexed citations
13.
Leng, Theodore, et al.. (2018). Automatic identification of referral-warranted diabetic retinopathy using deep learning on mobile phone images. Investigative Ophthalmology & Visual Science. 59(9). 1705–1705. 1 indexed citations
14.
Li, Zhong‐Qiu, et al.. (2018). Comparison of Automated Self-Refraction Using NETRA with Table-Mounted Autorefractor and Subjective Refraction in an Academic Optometry Clinic. Investigative Ophthalmology & Visual Science. 59(9). 3407–3407. 2 indexed citations
15.
Groth, Sylvia L., Zhongqiu Li, Sophia Y. Wang, et al.. (2018). Recombinant human nerve growth factor (rhNGF) eye drops for glaucoma: Interim results. Investigative Ophthalmology & Visual Science. 59(9). 1241–1241. 1 indexed citations
16.
Li, Zhixi, Stuart Keel, Chi Liu, et al.. (2018). An Automated Grading System for Detection of Vision-Threatening Referable Diabetic Retinopathy on the Basis of Color Fundus Photographs. Diabetes Care. 41(12). 2509–2516. 191 indexed citations
17.
Yu, Caroline, et al.. (2016). Eye Fatigue During TV Watching: An Infrared Oculography Study of Linearly vs. Circularly Polarized LCD TV. Investigative Ophthalmology & Visual Science. 57(12). 4591–4591. 1 indexed citations
18.
He, Lingmin, David Myung, Suzann Pershing, & Robert T. Chang. (2014). iPhone Photography of Eye Pathology for Remote Triage. Investigative Ophthalmology & Visual Science. 55(13). 4875–4875. 1 indexed citations
19.
Chang, Robert T., et al.. (2013). Novel Corneal Biomechanical Parameters in Myopes vs Emmetropes. Investigative Ophthalmology & Visual Science. 54(15). 1638–1638. 1 indexed citations
20.
Johung, Tessa, et al.. (2013). The Prevalence of Cirrus SD-OCT Ganglion Cell Segmentation Errors in High Myopes. Investigative Ophthalmology & Visual Science. 54(15). 4845–4845. 1 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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