Neural networks for predicting etiological diagnosis of uveitis
Study results suggest that developing models for accurately predicting the etiological diagnosis of uveitis with undetermined etiology based on clinical information is feasible. Such NN-MLP could be used for the etiological assessments of uveitis with unknown etiology.
Authors
- Laurent Kodjikian (ORCID: https://orcid.org/0000-0002-3908-6716)
- Lijuan Ren (ORCID: https://orcid.org/0000-0002-9948-9111)
- P. Sève (ORCID: https://orcid.org/0000-0003-3070-3842)
- Yvan Jamilloux (ORCID: https://orcid.org/0000-0001-5249-3650)
- R. Jacquot (ORCID: https://orcid.org/0000-0002-6084-995X)
- Antoine Duclos (ORCID: https://orcid.org/0000-0002-8915-4203)
- Tao Wang (ORCID: https://orcid.org/0000-0001-8100-6743)
- Dinu Stanescu
- Insaf Mellahk
Institutions
- Université Claude Bernard Lyon 1 (FR)
- Inserm (FR)
- Université Lumière Lyon 2 (FR)
- Chengdu University of Information Technology (CN)
- Sorbonne Université (FR)
- Hôpital de la Croix-Rousse (FR)
- Assistance Publique – Hôpitaux de Paris (FR)
- Hospices Civils de Lyon (FR)
- Health Services and Performance Research Laboratory (FR)
- Pitié-Salpêtrière Hospital (FR)
- Vinci (France) (FR)
- Decision & Information Sciences for Production Systems (FR)
- Research on healthcare performance (FR)
- Institut National des Sciences Appliquées de Lyon (FR)
- Université Jean Monnet (FR)
Publication Details
- Journal
- Eye
- Published
- 2024-12-20
- DOI
- https://doi.org/10.1038/s41433-024-03530-2
- Citations
- 4
- Primary Topic
- Ocular Diseases and Behçet’s Syndrome
- Type
- article
- Field-Weighted Citation Impact
- 2.15
Funders
- Hospices Civils de Lyon