Dun Jack Fu

3.5k total citations · 1 hit paper
41 papers, 2.1k citations indexed

About

Dun Jack Fu is a scholar working on Ophthalmology, Radiology, Nuclear Medicine and Imaging and Molecular Biology. According to data from OpenAlex, Dun Jack Fu has authored 41 papers receiving a total of 2.1k indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Ophthalmology, 30 papers in Radiology, Nuclear Medicine and Imaging and 4 papers in Molecular Biology. Recurrent topics in Dun Jack Fu's work include Retinal Imaging and Analysis (26 papers), Retinal Diseases and Treatments (24 papers) and Retinal and Optic Conditions (20 papers). Dun Jack Fu is often cited by papers focused on Retinal Imaging and Analysis (26 papers), Retinal Diseases and Treatments (24 papers) and Retinal and Optic Conditions (20 papers). Dun Jack Fu collaborates with scholars based in United Kingdom, United States and Switzerland. Dun Jack Fu's co-authors include Pearse A. Keane, Konstantinos Balaskas, Siegfried K. Wagner, Livia Faes, Xiaoxuan Liu, Alastair K. Denniston, Gabriella Moraes, Lucas M. Bachmann, Christoph Kern and Martin Schmid and has published in prestigious journals such as Scientific Reports, Ophthalmology and Journal of Investigative Dermatology.

In The Last Decade

Dun Jack Fu

39 papers receiving 2.1k 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.

A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis

2019 1.0k citations Xiaoxuan Liu, Livia Faes et al. The Lancet Digital Health profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Dun Jack Fu United Kingdom	17	1.2k	675	563	498	194	41	2.1k
Christoph Kern Germany	14	983 0.8×	539 0.8×	494 0.9×	449 0.9×	158 0.8×	36	1.8k
Livia Faes United Kingdom	20	1.6k 1.4×	1.0k 1.5×	651 1.2×	579 1.2×	222 1.1×	66	2.7k
Gabriella Moraes United Kingdom	12	883 0.8×	375 0.6×	510 0.9×	481 1.0×	158 0.8×	22	1.6k
Avinash V. Varadarajan United States	8	1.1k 0.9×	732 1.1×	219 0.4×	298 0.6×	120 0.6×	10	1.6k
Katy Blumer United States	6	838 0.7×	494 0.7×	215 0.4×	328 0.7×	171 0.9×	8	1.5k
Konstantinos Balaskas United Kingdom	31	2.2k 1.9×	1.9k 2.8×	676 1.2×	590 1.2×	303 1.6×	136	3.8k
Ken Chang United States	23	1.4k 1.2×	225 0.3×	283 0.5×	521 1.0×	253 1.3×	51	2.2k
Edward Korot United Kingdom	14	760 0.6×	550 0.8×	205 0.4×	219 0.4×	74 0.4×	38	1.2k
Cecilia S. Lee United States	32	2.0k 1.7×	2.4k 3.5×	143 0.3×	208 0.4×	374 1.9×	121	3.5k
Joseph R. Ledsam United Kingdom	8	725 0.6×	85 0.1×	496 0.9×	491 1.0×	194 1.0×	14	1.9k

Countries citing papers authored by Dun Jack Fu

Since Specialization

Citations

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

Fields of papers citing papers by Dun Jack Fu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dun Jack Fu. 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 Dun Jack Fu. The network helps show where Dun Jack Fu may publish in the future.

Co-authorship network of co-authors of Dun Jack Fu

This figure shows the co-authorship network connecting the top 25 collaborators of Dun Jack Fu. A scholar is included among the top collaborators of Dun Jack Fu 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 Dun Jack Fu. Dun Jack Fu 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

Fu, Dun Jack, et al.. (2025). Validation of a Visual Field Prediction Tool for Glaucoma: A Multicenter Study Involving Patients With Glaucoma in the United Kingdom. American Journal of Ophthalmology. 272. 87–97. 1 indexed citations

Fu, Dun Jack, et al.. (2024). Survival of medical treatment success in primary open-angle glaucoma and ocular hypertension. British Journal of Ophthalmology. 108(12). 1701–1707. 4 indexed citations

Fu, Dun Jack, Gabriella Moraes, Konstantinos Balaskas, et al.. (2022). Evaluating an automated machine learning model that predicts visual acuity outcomes in patients with neovascular age-related macular degeneration. Graefe s Archive for Clinical and Experimental Ophthalmology. 260(8). 2461–2473. 19 indexed citations

Fu, Dun Jack, et al.. (2022). Burden of Glaucoma in the United Kingdom. Ophthalmology Glaucoma. 6(1). 106–115. 19 indexed citations

Balaskas, Konstantinos, Tiarnán D L Keenan, Livia Faes, et al.. (2022). Prediction of visual function from automatically quantified optical coherence tomography biomarkers in patients with geographic atrophy using machine learning. Scientific Reports. 12(1). 15565–15565. 16 indexed citations

Fu, Dun Jack, Livia Faes, Siegfried K. Wagner, et al.. (2021). Predicting Incremental and Future Visual Change in Neovascular Age-Related Macular Degeneration Using Deep Learning. Ophthalmology Retina. 5(11). 1074–1084. 33 indexed citations

Zhang, Gongyu, Dun Jack Fu, Bart Liefers, et al.. (2021). Clinically relevant deep learning for detection and quantification of geographic atrophy from optical coherence tomography: a model development and external validation study. The Lancet Digital Health. 3(10). e665–e675. 60 indexed citations

Kern, Christoph, Dun Jack Fu, Benedikt Schworm, et al.. (2021). Big data simulations for capacity improvement in a general ophthalmology clinic. Graefe s Archive for Clinical and Experimental Ophthalmology. 259(5). 1289–1296. 8 indexed citations

Moraes, Gabriella, Dun Jack Fu, Hagar Khalid, et al.. (2020). Quantitative Analysis of OCT for Neovascular Age-Related Macular Degeneration Using Deep Learning. Ophthalmology. 128(5). 693–705. 94 indexed citations

10.

Kern, Christoph, Dun Jack Fu, Josef Huemer, et al.. (2020). An open-source data set of anti-VEGF therapy in diabetic macular oedema patients over 4 years and their visual acuity outcomes. Eye. 35(5). 1354–1364. 5 indexed citations

11.

Zhang, Gongyu, Edward Korot, Reena Chopra, et al.. (2020). Optimising Treatment of Neovascular Age-related Macular Degeneration using Reinforcement Learning. Investigative Ophthalmology & Visual Science. 61(7). 1628–1628. 1 indexed citations

12.

Faes, Livia, Dun Jack Fu, Josef Huemer, et al.. (2020). A virtual-clinic pathway for patients referred from a national diabetes eye screening programme reduces service demands whilst maintaining quality of care. Eye. 35(8). 2260–2269. 12 indexed citations

13.

Chopra, Reena, Gabriella Moraes, Dun Jack Fu, et al.. (2020). Quantitative analysis of change in retinal tissues in neovascular age-related macular degeneration using artificial intelligence. Investigative Ophthalmology & Visual Science. 61(7). 1152–1152. 1 indexed citations

14.

Wagner, Siegfried K., Dun Jack Fu, Livia Faes, et al.. (2020). Insights into Systemic Disease through Retinal Imaging-Based Oculomics. Translational Vision Science & Technology. 9(2). 6–6. 149 indexed citations

15.

Kern, Christoph, Dun Jack Fu, Benedikt Schworm, et al.. (2020). <p>Two Year Visual Acuity and Structural Outcomes in Patients with Diabetic Macular Oedema Treated with Intravitreal Aflibercept – A Retrospective Cohort Study</p>. Clinical ophthalmology. Volume 14. 533–541. 8 indexed citations

16.

Korot, Edward, Siegfried K. Wagner, Livia Faes, et al.. (2020). AI building AI: Deep Learning Detection of Referable Diabetic Retinopathy Sans-coding. Investigative Ophthalmology & Visual Science. 61(7). 2025–2025. 2 indexed citations

17.

Fasler, Katrin, Dun Jack Fu, Gabriella Moraes, et al.. (2019). Moorfields AMD database report 2: fellow eye involvement with neovascular age-related macular degeneration. British Journal of Ophthalmology. 104(5). 684–690. 23 indexed citations

18.

Wagner, Siegfried K., Praveen J. Patel, Dun Jack Fu, et al.. (2019). Effect of total anti-VEGF treatment exposure on patterns of choroidal neovascularisation assessed by optical coherence tomography angiography in age-related macular degeneration: a retrospective case series. BMJ Open Ophthalmology. 4(1). e000244–e000244. 6 indexed citations

19.

Fasler, Katrin, Gabriella Moraes, Siegfried K. Wagner, et al.. (2019). One- and two-year visual outcomes from the Moorfields age-related macular degeneration database: a retrospective cohort study and an open science resource. BMJ Open. 9(6). e027441–e027441. 27 indexed citations

20.

Sharma, Srilakshmi M., Dun Jack Fu, & Kanmin Xue. (2017). A Review of the Landscape of Targeted Immunomodulatory Therapies for Non-Infectious Uveitis. Ophthalmology and Therapy. 7(1). 1–17. 12 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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