Ronan Danno
- Radiology, Nuclear Medicine and Imaging top 2%
- Ophthalmology top 1%
- Computer Vision and Pattern Recognition top 5%
- Health Information Management top 2%
- Artificial Intelligence
- Co-authors
- Guy CazuguelMathieu LamardAli ErginayBeatriz MarcoteguiPascale MassinGuillaume ThibaultÉtienne DecencièreGwenolé Quellec
- Topics
- Retinal Imaging and Analysis (6 papers)Retinal Diseases and Treatments (3 papers)Atmospheric and Environmental Gas Dynamics (3 papers)
- Partner nations
- FranceUnited States
In The Last Decade
Ronan Danno
10 papers receiving 550 citations
Hit Papers
Peers
Comparison fields: 5 of 50
- Radiology, Nuclear Medicine and Imaging 527
- Ophthalmology 401
- Computer Vision and Pattern Recognition 263
- Health Information Management 70
- Artificial Intelligence 53
Countries citing papers authored by Ronan Danno
This map shows the geographic impact of Ronan Danno'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 Ronan Danno with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ronan Danno more than expected).
Fields of papers citing papers by Ronan Danno
This network shows the impact of papers produced by Ronan Danno. 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 Ronan Danno. The network helps show where Ronan Danno may publish in the future.
Co-authorship network of co-authors of Ronan Danno
This figure shows the co-authorship network connecting the top 25 collaborators of Ronan Danno. A scholar is included among the top collaborators of Ronan Danno 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 Ronan Danno. Ronan Danno is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Using Artificial Intelligence to detect glaucoma and Age related Macula Degeneration | 1 |
| 2 | Prediction of Geographic Atrophy progression by deep learning applied to retinal imaging | 2 |
| 3 | Repeatability and Validation of Scheimpflug Scleral Data | 3 |
| 4 | 12 | |
| 5 | Modeling the limbus as an elliptical toric to optimize scleral lens fitting | 1 |
| 6 | 6 | |
| 7 | 11 | |
| 8 | 197 | |
| 9 | Iconography : TeleOphta: Machine learning and image processing methods for teleophthalmology | 2 |
| 10 | TeleOphta: Machine learning and image processing methods for teleophthalmologybreakdown → | 343 |
| 11 | Automatic Detection Of Exudates In Color Retinal Images | 2 |
About Ronan Danno
Ronan Danno is a scholar working on Ophthalmology, Radiology, Nuclear Medicine and Imaging and Health Information Management, having authored 11 papers that have together received 580 indexed citations. Recurring topics across this work include Retinal Imaging and Analysis (6 papers), Retinal Diseases and Treatments (3 papers) and Atmospheric and Environmental Gas Dynamics (3 papers). The work is most often cited by research in Ophthalmology (401 citations), Radiology, Nuclear Medicine and Imaging (527 citations) and Health Information Management (70 citations). Ronan Danno has collaborated with scholars based in France and United States. Frequent co-authors include Guy Cazuguel, Mathieu Lamard, Ali Erginay, Beatriz Marcotegui, Pascale Massin, Guillaume Thibault, Étienne Decencière, Gwenolé Quellec, A. Chabouis and B. Laÿ. Their work appears in journals such as Investigative Ophthalmology & Visual Science, Medical Image Analysis and IRBM.
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.