Mark Christopher

2.0k total citations · 1 hit paper
55 papers, 1.3k citations indexed

About

Mark Christopher is a scholar working on Radiology, Nuclear Medicine and Imaging, Ophthalmology and Molecular Biology. According to data from OpenAlex, Mark Christopher has authored 55 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 48 papers in Radiology, Nuclear Medicine and Imaging, 47 papers in Ophthalmology and 5 papers in Molecular Biology. Recurrent topics in Mark Christopher's work include Glaucoma and retinal disorders (46 papers), Retinal Imaging and Analysis (42 papers) and Retinal Diseases and Treatments (34 papers). Mark Christopher is often cited by papers focused on Glaucoma and retinal disorders (46 papers), Retinal Imaging and Analysis (42 papers) and Retinal Diseases and Treatments (34 papers). Mark Christopher collaborates with scholars based in United States, Germany and Japan. Mark Christopher's co-authors include Linda M. Zangwill, Robert N. Weinreb, Christopher Bowd, Akram Belghith, Michael H. Goldbaum, Christopher A. Girkin, Jeffrey M. Liebmann, Sasan Moghimi, Massimo A. Fazio and Rafaella C. Penteado and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and Ophthalmology.

In The Last Decade

Mark Christopher

51 papers receiving 1.3k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs

2018 224 citations Mark Christopher, Akram Belghith et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Mark Christopher United States	18	1.1k	1.1k	140	95	80	55	1.3k
Yuri Fujino Japan	19	1.2k 1.0×	1.1k 1.0×	110 0.8×	69 0.7×	51 0.6×	72	1.3k
Alfonso Antón Spain	15	896 0.8×	790 0.7×	111 0.8×	58 0.6×	60 0.8×	58	1.0k
Amir Sadeghipour Austria	15	1.4k 1.3×	1.5k 1.4×	182 1.3×	188 2.0×	73 0.9×	43	1.8k
Akram Belghith United States	24	2.5k 2.2×	2.3k 2.1×	170 1.2×	244 2.6×	85 1.1×	87	2.7k
Atsuya Miki Japan	23	1.5k 1.4×	1.3k 1.2×	49 0.3×	123 1.3×	115 1.4×	77	1.7k
Hitoshi Tabuchi Japan	27	1.6k 1.5×	1.5k 1.4×	69 0.5×	121 1.3×	78 1.0×	156	2.0k
Alauddin Bhuiyan Australia	19	885 0.8×	1.0k 1.0×	471 3.4×	121 1.3×	47 0.6×	66	1.3k
Ce Zheng China	18	1.2k 1.1×	1.1k 1.0×	130 0.9×	282 3.0×	45 0.6×	49	1.3k
Massimo A. Fazio United States	20	1.4k 1.2×	1.3k 1.2×	71 0.5×	103 1.1×	92 1.1×	100	1.6k
Pascal W. Hasler Switzerland	16	986 0.9×	645 0.6×	31 0.2×	122 1.3×	134 1.7×	59	1.2k

Countries citing papers authored by Mark Christopher

Since Specialization

Citations

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

Fields of papers citing papers by Mark Christopher

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mark Christopher

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

All Works

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20 of 20 papers shown

Bowd, Christopher, Akram Belghith, Michael H. Goldbaum, et al.. (2025). Deep Learning Approach Predicts Longitudinal Retinal Nerve Fiber Layer Thickness Changes. Bioengineering. 12(2). 139–139.

Christopher, Mark, Rubèn González, Bharanidharan Radha Saseendrakumar, et al.. (2024). Proactive Decision Support for Glaucoma Treatment: Predicting Surgical Interventions with Clinically Available Data. Bioengineering. 11(2). 140–140. 7 indexed citations

Bowd, Christopher, Akram Belghith, Michael H. Goldbaum, et al.. (2024). Glaucoma Detection and Feature Identification via GPT-4V Fundus Image Analysis. SHILAP Revista de lepidopterología. 5(2). 100667–100667. 3 indexed citations

Micheletti, Eleonora, Nevin W. El-Nimri, Takashi Nishida, et al.. (2023). Central visual field damage in glaucoma eyes with choroidal microvasculature dropout with and without high axial myopia. British Journal of Ophthalmology. 108(3). 372–379. 8 indexed citations

Mahmoudinezhad, Golnoush, Sasan Moghimi, Siavash Beheshtaein, et al.. (2023). Deep Learning Estimation of 10-2 Visual Field Map Based on Macular Optical Coherence Tomography Angiography Measurements. American Journal of Ophthalmology. 257. 187–200. 3 indexed citations

Weinreb, Robert N., et al.. (2023). Review of Visualization Approaches in Deep Learning Models of Glaucoma. Asia-Pacific Journal of Ophthalmology. 12(4). 392–401. 7 indexed citations

Christopher, Mark, James A. Proudfoot, Christopher Bowd, et al.. (2022). A Deep Learning Approach to Improve Retinal Structural Predictions and Aid Glaucoma Neuroprotective Clinical Trial Design. Ophthalmology Glaucoma. 6(2). 147–159. 11 indexed citations

Kamalipour, Alireza, Sasan Moghimi, Vahid Mohammadzadeh, et al.. (2022). Combining Optical Coherence Tomography and Optical Coherence Tomography Angiography Longitudinal Data for the Detection of Visual Field Progression in Glaucoma. American Journal of Ophthalmology. 246. 141–154. 15 indexed citations

Rezapour, Jasmin, Christopher Bowd, Akram Belghith, et al.. (2022). Macula structural and vascular differences in glaucoma eyes with and without high axial myopia. British Journal of Ophthalmology. 107(9). 1286–1294. 15 indexed citations

10.

Kamalipour, Alireza, Sasan Moghimi, Takashi Nishida, et al.. (2022). Deep Learning Estimation of 10-2 Visual Field Map Based on Circumpapillary Retinal Nerve Fiber Layer Thickness Measurements. American Journal of Ophthalmology. 246. 163–173. 15 indexed citations

11.

Fan, Rui, Christopher Bowd, Mark Christopher, et al.. (2022). Detecting Glaucoma from Fundus Photographs Using Deep Learning without Convolutions. SHILAP Revista de lepidopterología. 3(1). 100233–100233. 74 indexed citations

12.

Rezapour, Jasmin, James A. Proudfoot, Christopher Bowd, et al.. (2021). Bruch Membrane Opening Detection Accuracy in Healthy Eyes and Eyes With Glaucoma With and Without Axial High Myopia in an American and Korean Cohort. American Journal of Ophthalmology. 237. 221–234. 17 indexed citations

13.

Christopher, Mark, Christopher Bowd, James A. Proudfoot, et al.. (2021). Performance of Deep Learning Models to Detect Glaucoma Using Unsegmented Radial and Circle OCT Scans of the Optic Nerve Head. Investigative Ophthalmology & Visual Science. 62(8). 1014–1014. 1 indexed citations

14.

Rezapour, Jasmin, Christopher Bowd, Akram Belghith, et al.. (2021). Macular thickness and vessel density in glaucoma eyes with and without high axial myopia. Investigative Ophthalmology & Visual Science. 62(8). 2431–2431. 1 indexed citations

15.

Christopher, Mark, Christopher Bowd, Akram Belghith, et al.. (2020). Deep Learning Models Based on Unsegmented OCT RNFL Circle Scans Provide Accurate Detection of Glaucoma and High Resolution Prediction of Visual Field Damage. Investigative Ophthalmology & Visual Science. 61(7). 1439–1439. 2 indexed citations

16.

Ghahari, Elham, Christopher Bowd, Linda M. Zangwill, et al.. (2019). Association of Macular and Circumpapillary Microvasculature with Visual Field Sensitivity in Advanced Glaucoma. American Journal of Ophthalmology. 204. 51–61. 47 indexed citations

17.

Moghimi, Sasan, Linda M. Zangwill, Rafaella C. Penteado, et al.. (2018). Macular and Optic Nerve Head Vessel Density and Progressive Retinal Nerve Fiber Layer Loss in Glaucoma. Ophthalmology. 125(11). 1720–1728. 119 indexed citations

18.

Hou, Huiyuan, T. Shoji, Linda M. Zangwill, et al.. (2018). Progression of Primary Open-Angle Glaucoma in Diabetic and Nondiabetic Patients. American Journal of Ophthalmology. 189. 1–9. 30 indexed citations

19.

Hedberg‐Buenz, Adam, Mark Christopher, Kimberly A. Fernandes, et al.. (2015). Quantitative measurement of retinal ganglion cell populations via histology-based random forest classification. Experimental Eye Research. 146. 370–385. 25 indexed citations

20.

Murphy, William D., Gregory C. Hurst, Jeffrey L. Duerk, et al.. (1991). Atypical appearance of lipomatous tumors on MR images: High signal intensity with fat‐suppression STIR sequences. Journal of Magnetic Resonance Imaging. 1(4). 477–480. 10 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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