Dan Miléa

8.8k total citations
197 papers, 5.1k citations indexed

About

Dan Miléa is a scholar working on Ophthalmology, Radiology, Nuclear Medicine and Imaging and Molecular Biology. According to data from OpenAlex, Dan Miléa has authored 197 papers receiving a total of 5.1k indexed citations (citations by other indexed papers that have themselves been cited), including 111 papers in Ophthalmology, 51 papers in Radiology, Nuclear Medicine and Imaging and 49 papers in Molecular Biology. Recurrent topics in Dan Miléa's work include Glaucoma and retinal disorders (56 papers), Cerebral Venous Sinus Thrombosis (32 papers) and Retinal Diseases and Treatments (31 papers). Dan Miléa is often cited by papers focused on Glaucoma and retinal disorders (56 papers), Cerebral Venous Sinus Thrombosis (32 papers) and Retinal Diseases and Treatments (31 papers). Dan Miléa collaborates with scholars based in Singapore, France and Denmark. Dan Miléa's co-authors include Charles Pierrot‐Deseilligny, C. Pierrot‐Deseilligny, Pascal Reynier, Patrizia Amati‐Bonneau, Raymond P. Najjar, Dominique Bonneau, Vincent Procaccio, Tin Aung, Birgit Sander and Guy Lenaers and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and NeuroImage.

In The Last Decade

Dan Miléa

187 papers receiving 5.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Dan Miléa Singapore 40 2.0k 1.6k 1.2k 905 713 197 5.1k
Vincenzo Parisi Italy 46 3.3k 1.6× 1.9k 1.2× 1.7k 1.4× 505 0.6× 788 1.1× 195 6.5k
Irène Gottlob United Kingdom 41 2.0k 1.0× 1.7k 1.1× 940 0.8× 428 0.5× 968 1.4× 232 5.0k
Randy H. Kardon United States 51 5.4k 2.7× 1.9k 1.2× 2.2k 1.8× 2.1k 2.3× 672 0.9× 287 9.5k
Michael Belkin Israel 42 2.3k 1.2× 1.4k 0.9× 1.9k 1.6× 234 0.3× 638 0.9× 201 5.9k
Alexander Klistorner Australia 41 2.2k 1.1× 1.6k 1.0× 963 0.8× 655 0.7× 668 0.9× 162 4.8k
Daphne L. McCulloch United Kingdom 32 2.3k 1.1× 2.7k 1.7× 1.1k 0.9× 243 0.3× 1.4k 2.0× 97 5.4k
Selim Orgül Switzerland 34 3.9k 1.9× 833 0.5× 2.2k 1.8× 413 0.5× 225 0.3× 116 5.4k
Mitchell Brigell United States 30 3.2k 1.6× 3.6k 2.3× 1.2k 1.0× 292 0.3× 1.6k 2.2× 82 6.4k
Chiara La Morgia Italy 35 1.1k 0.5× 2.4k 1.5× 405 0.3× 452 0.5× 297 0.4× 115 3.7k
Brad Fortune United States 41 4.6k 2.3× 1.9k 1.2× 2.7k 2.2× 343 0.4× 251 0.4× 156 5.3k

Countries citing papers authored by Dan Miléa

Since Specialization
Citations

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

Fields of papers citing papers by Dan Miléa

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dan Miléa

This figure shows the co-authorship network connecting the top 25 collaborators of Dan Miléa. A scholar is included among the top collaborators of Dan Miléa 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 Dan Miléa. Dan Miléa 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.
Singhal, Shweta, et al.. (2025). Deep Learning–Based Detection of Papilledema on Retinal Photographs From Handheld Cameras: A Prospective Study. Journal of Neuro-Ophthalmology. 46(1). 98–104.
2.
Duron, Loïc, P. Koskas, Émilie Poirion, et al.. (2025). Synthetic MRI for Detecting Abnormal Signals in the Optic Nerves. Investigative Radiology. 61(4). 261–271. 1 indexed citations
3.
Tan, Bingyao, Damon Wing Kee Wong, Gerhard Garhöfer, et al.. (2024). Optical coherence tomography angiography of the retina and choroid in systemic diseases. Progress in Retinal and Eye Research. 103. 101292–101292. 14 indexed citations
4.
Ferré, Marc, Valérie Desquiret‐Dumas, Alexis Descatha, et al.. (2024). Genetic susceptibility to optic neuropathy in patients with alcohol use disorder. Journal of Translational Medicine. 22(1). 495–495.
5.
Najjar, Raymond P., Zhiqun Tang, Clare L. Fraser, et al.. (2024). A Deep Learning Approach for Accurate Discrimination Between Optic Disc Drusen and Papilledema on Fundus Photographs. Journal of Neuro-Ophthalmology. 44(4). 454–461. 1 indexed citations
6.
Najjar, Raymond P., et al.. (2023). A Deep Learning System for Automated Quality Evaluation of Optic Disc Photographs in Neuro-Ophthalmic Disorders. Diagnostics. 13(1). 160–160. 3 indexed citations
7.
Vasseneix, Caroline, Simon Nusinovici, Xinxing Xu, et al.. (2023). Deep Learning System Outperforms Clinicians in Identifying Optic Disc Abnormalities. Journal of Neuro-Ophthalmology. 43(2). 159–167. 8 indexed citations
8.
Cheung, Chui Ming Gemmy, Eva Fenwick, Augustinus Laude, et al.. (2022). Associations between age‐related macular degeneration and sleep dysfunction: A systematic review. Clinical and Experimental Ophthalmology. 50(9). 1025–1037. 7 indexed citations
9.
10.
Ferré, Marc, et al.. (2021). Dominant Optic Atrophy: How to Determine the Pathogenicity of Novel Variants?. Journal of Neuro-Ophthalmology. 42(2). 149–153. 1 indexed citations
11.
Biousse, Valérie, et al.. (2021). Deep learning for automated quality assessment of optic disc images in neuro-ophthalmic conditions.. Investigative Ophthalmology & Visual Science. 62(8). 2148–2148.
12.
Hoang, Quan V., Tin A. Tun, Chee Wai Wong, et al.. (2020). Differing Optic Nerve Head Strains Comparing Low, High and Pathologic Myopia Eyes. Investigative Ophthalmology & Visual Science. 61(7). 2679–2679. 1 indexed citations
13.
Wang, Xiaofei, Tin A. Tun, Dan Miléa, et al.. (2020). Adduction Induces Abnormally Large Optic Nerve Head Strains in Normal Tension Glaucoma Subjects. Investigative Ophthalmology & Visual Science. 61(7). 1005–1005. 2 indexed citations
14.
Tan, Tien‐En, Gavin Tan, Anna C. S. Tan, et al.. (2020). Handheld chromatic pupillometry detects preclinical retinal dysfunction in patients with diabetes mellitus. Investigative Ophthalmology & Visual Science. 61(7). 5039–5039. 1 indexed citations
15.
Wang, Xiaofei, Helmut Rumpel, Mani Baskaran, et al.. (2019). Optic Nerve Tortuosity and Globe Proptosis in Normal and Glaucoma Subjects. Journal of Glaucoma. 28(8). 691–696. 17 indexed citations
16.
Angebault, Claire, Jérémy Fauconnier, Simone Patergnani, et al.. (2018). ER-mitochondria cross-talk is regulated by the Ca 2+ sensor NCS1 and is impaired in Wolfram syndrome. Science Signaling. 11(553). 105 indexed citations
17.
Wang, Xiaofei, et al.. (2017). Predictions of Optic Nerve Traction Forces and Peripapillary Tissue Stresses Following Horizontal Eye Movements. Investigative Ophthalmology & Visual Science. 58(4). 2044–2044. 75 indexed citations
18.
Grønskov, Karen, et al.. (2011). Genomic deletions in OPA1 in Danish patients with autosomal dominant optic atrophy. BMC Medical Genetics. 12(1). 49–49. 18 indexed citations
19.
Chevrollier, Arnaud, Virginie Guillet, Dominique Loiseau, et al.. (2008). Hereditary optic neuropathies share a common mitochondrial coupling defect. Annals of Neurology. 63(6). 794–798. 94 indexed citations
20.
Bodaghi, Bahram, D. Lê Thi Huong, Dan Miléa, et al.. (2003). Pseudotumor cerebri associated with Sjögren's syndrome. Graefe s Archive for Clinical and Experimental Ophthalmology. 241(4). 339–342. 6 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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