Diagnostic des uvéites : présent et futur
Abstract
Uveitis encompasses a heterogeneous spectrum of etiologies, including systemic inflammatory diseases, infections, specific ophthalmologic entities, and pseudo-uveitis. The diagnostic approach relies on an algorithmic strategy aimed at the early identification of a curable etiology and/or an underlying condition suitable for targeted therapy, while taking into account the severity and relative frequency of the various causes. The prospective ULISSE study showed that a standardized diagnostic approach based on the anatomo-clinical classification of uveitis achieved a diagnostic yield comparable to unrestricted testing, while substantially reducing both the number and cost of investigations. Etiological diagnosis is primarily based on anatomo-clinical analysis of uveitis, integrating the site of inflammation, disease course, laterality, and specific ophthalmologic features. Subsequently, consideration of epidemiological and demographic factors, medical history, and extra-ophthalmologic clinical examination allows further refinement of the diagnostic workup. Complementary investigations should be prioritized and targeted, except for a minimal systematic baseline evaluation, as the diagnostic yield of non-directed testing is very low. Clinical decision-support tools based on artificial intelligence (AI) represent a major advancement. Machine learning models integrating clinical, ophthalmologic, laboratory, and imaging data available from the initial consultation achieve etiological prediction performances comparable to those of experts with access to a complete diagnostic workup. These multimodal AI models could transform diagnostic strategies by enabling a personalized etiological assessment based on the prioritization and dynamic adaptation of complementary investigations. However, their implementation in routine clinical practice will require prior medico-economic validation through controlled comparative studies.
Related
- The past, present and future of genomic scans for selection.
- Present and Future Climatic Suitability of Castilleja tenuiflora in Mexico
- Past, present, and future of SSR molecular markers in bee studies
- The anti-dyskinetic and the anti-parkinsonian effects of sub-anesthetic ketamine are also present in female preclinical models
- Methane oxidation genes are only present in bioaerosols at sites with high methane concentrations
BibTeX
@article{jacquot2026diagnostic,
title = {{Diagnostic des uvéites : présent et futur}},
author = {Robin Jacquot and Thomas El-Jammal and Yvan Jamilloux and François Frapart and Thibaud Mathis and Laurent Kodjikian and Pascal Seve},
journal = {La Revue de Médecine Interne},
year = {2026},
month = sep,
doi = {10.1016/j.revmed.2026.07.005},
url = {https://hal.science/hal-05750618v1},
note = {CC BY},
}
RIS
TY - JOUR
TI - Diagnostic des uvéites : présent et futur
AU - Robin Jacquot
AU - Thomas El-Jammal
AU - Yvan Jamilloux
AU - François Frapart
AU - Thibaud Mathis
AU - Laurent Kodjikian
AU - Pascal Seve
PY - 2026
DA - 2026/09/15
JO - La Revue de Médecine Interne
DO - 10.1016/j.revmed.2026.07.005
UR - https://hal.science/hal-05750618v1
AB - Uveitis encompasses a heterogeneous spectrum of etiologies, including systemic inflammatory diseases, infections, specific ophthalmologic entities, and pseudo-uveitis. The diagnostic approach relies on an algorithmic strategy aimed at the early identification of a curable etiology and/or an underlying condition suitable for targeted therapy, while taking into account the severity and relative frequency of the various causes. The prospective ULISSE study showed that a standardized diagnostic approach based on the anatomo-clinical classification of uveitis achieved a diagnostic yield comparable to unrestricted testing, while substantially reducing both the number and cost of investigations. Etiological diagnosis is primarily based on anatomo-clinical analysis of uveitis, integrating the site of inflammation, disease course, laterality, and specific ophthalmologic features. Subsequently, consideration of epidemiological and demographic factors, medical history, and extra-ophthalmologic clinical examination allows further refinement of the diagnostic workup. Complementary investigations should be prioritized and targeted, except for a minimal systematic baseline evaluation, as the diagnostic yield of non-directed testing is very low. Clinical decision-support tools based on artificial intelligence (AI) represent a major advancement. Machine learning models integrating clinical, ophthalmologic, laboratory, and imaging data available from the initial consultation achieve etiological prediction performances comparable to those of experts with access to a complete diagnostic workup. These multimodal AI models could transform diagnostic strategies by enabling a personalized etiological assessment based on the prioritization and dynamic adaptation of complementary investigations. However, their implementation in routine clinical practice will require prior medico-economic validation through controlled comparative studies.
SN - 0248-8663
SN - 1768-3122
ER -