Livia Faes

7.3k total citations · 2 hit papers
66 papers, 2.7k citations indexed

About

Livia Faes is a scholar working on Radiology, Nuclear Medicine and Imaging, Ophthalmology and Health Informatics. According to data from OpenAlex, Livia Faes has authored 66 papers receiving a total of 2.7k indexed citations (citations by other indexed papers that have themselves been cited), including 49 papers in Radiology, Nuclear Medicine and Imaging, 48 papers in Ophthalmology and 7 papers in Health Informatics. Recurrent topics in Livia Faes's work include Retinal Imaging and Analysis (42 papers), Retinal Diseases and Treatments (35 papers) and Retinal and Optic Conditions (30 papers). Livia Faes is often cited by papers focused on Retinal Imaging and Analysis (42 papers), Retinal Diseases and Treatments (35 papers) and Retinal and Optic Conditions (30 papers). Livia Faes collaborates with scholars based in United Kingdom, Switzerland and United States. Livia Faes's co-authors include Pearse A. Keane, Siegfried K. Wagner, Lucas M. Bachmann, Konstantinos Balaskas, Alastair K. Denniston, Xiaoxuan Liu, Dun Jack Fu, Martin Schmid, Gabriella Moraes and Christoph Kern and has published in prestigious journals such as PLoS ONE, Scientific Reports and Ophthalmology.

In The Last Decade

Livia Faes

60 papers receiving 2.6k citations

Hit Papers

A comparison of deep learning performance against health-... 2019 2026 2021 2023 2019 2020 250 500 750 1000
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.

  1. 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. profile →
  2. A global review of publicly available datasets for ophthalmological imaging: barriers to access, usability, and generalisability
    2020 196 citations Xiaoxuan Liu, Edward Korot et al. profile →

Peers

Livia Faes
Siegfried K. Wagner United Kingdom
Dun Jack Fu United Kingdom
Konstantinos Balaskas United Kingdom
Christoph Kern Germany
Gabriella Moraes United Kingdom
Avinash V. Varadarajan United States
T. Madams United States
Katy Blumer United States
Derek Wu Canada
Arunachalam Narayanaswamy United States
Siegfried K. Wagner United Kingdom
Livia Faes
Citations per year, relative to Livia Faes Livia Faes (= 1×) peers Siegfried K. Wagner

Countries citing papers authored by Livia Faes

Since Specialization
Citations

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

Fields of papers citing papers by Livia Faes

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Livia Faes

This figure shows the co-authorship network connecting the top 25 collaborators of Livia Faes. A scholar is included among the top collaborators of Livia Faes 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 Livia Faes. Livia Faes 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
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Faes, Livia, Peter M. Maloca, Katja Hatz, et al.. (2022). Transforming ophthalmology in the digital century—new care models with added value for patients. Eye. 37(11). 2172–2175. 3 indexed citations
3.
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Huemer, Josef, Hagar Khalid, Daniel Ferraz, et al.. (2021). Re-evaluating diabetic papillopathy using optical coherence tomography and inner retinal sublayer analysis. Eye. 36(7). 1476–1485. 7 indexed citations
5.
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
6.
Korot, Edward, Daniel Ferraz, Siegfried K. Wagner, et al.. (2021). Code-free deep learning for multi-modality medical image classification. Nature Machine Intelligence. 3(4). 288–298. 115 indexed citations
7.
Bachmann, Lucas M., Livia Faes, Martin Schmid, et al.. (2021). Visual outcomes and treatment adherence of patients with macular pathology using a mobile hyperacuity home-monitoring app: a matched-pair analysis. BMJ Open. 11(12). e056940–e056940. 13 indexed citations
8.
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
9.
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
10.
Balaskas, Konstantinos, Pearse A. Keane, Siegfried K. Wagner, et al.. (2020). Automated classification of Retinopathy of Prematurity RetCam images using the Apple CreateML platform: gradeable versus ungradeable image classification. Investigative Ophthalmology & Visual Science. 61(7). 2026–2026. 1 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.
Faes, Livia, Lucas M. Bachmann, & Dawn A. Sim. (2020). Home monitoring as a useful extension of modern tele-ophthalmology. Eye. 34(11). 1950–1953. 22 indexed citations
14.
Liu, Xiaoxuan, Samantha Cruz Rivera, Livia Faes, et al.. (2020). CONSORT-AI and SPIRIT-AI: New Reporting Guidelines for Clinical Trials and Trial Protocols for Artificial Intelligence Interventions. Investigative Ophthalmology & Visual Science. 61(7). 1617–1617. 3 indexed citations
15.
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
16.
Faes, Livia, Nicolas S. Bodmer, Pearse A. Keane, et al.. (2019). Test performance of optical coherence tomography angiography in detecting retinal diseases: a systematic review and meta-analysis. Eye. 33(8). 1327–1338. 7 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.
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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.
Wagner, Siegfried K., Reena Chopra, Joseph R. Ledsam, et al.. (2019). Diagnostic accuracy and interobserver variability of macular disease evaluation using optical coherence tomography. Investigative Ophthalmology & Visual Science. 60(9). 1849–1849. 2 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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