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Novel Artificial Intelligence-Based Quantification of Anterior Chamber Inflammation Using Vision Transformers

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dc.creatorAgrawal, Rupeshspa
dc.creatorDe La Torre Cifuentes, Ligia Alejandraspa
dc.creatorLee, Bernettspa
dc.creatorRodríguez-Camelo, Laura Danielaspa
dc.creatorShannon, Choo Sherielspa
dc.creatorLoh, Nicholas Chiehspa
dc.creatorOo, Hnin Hninspa
dc.creatorShchurov, Leonidspa
dc.creatorWei, Xinspa
dc.creatorHudlikar, Atharvaspa
dc.creatorBoon, Joeweespa
dc.creatorRojas-Carabali, Williamspa
dc.creatorGutiérrez-Sinisterra, Lauraspa
dc.creatorCifuentes-González, Carlosspa
dc.date.accessioned2025-07-21T16:50:39Z
dc.date.available2025-07-21T16:50:39Z
dc.date.created2025-05-01spa
dc.date.issued2025-05-01spa
dc.description.abstractPurpose: Quantitative assessment of inflammation is critical for the accurate diagnosis and effective management of uveitis. This study aims to introduce a novel three-dimensional vision transformer approach using anterior segment optical coherence tomography (AS-OCT) to quantify anterior chamber (AC) inflammation in uveitis patients. Methods: This cross-sectional study was conducted from January 2022 to December 2023 at a single tertiary eye center in Singapore, analyzing 830 AS-OCT B-scans from 180 participants, including uveitis patients at various stages of inflammation and healthy controls. The primary outcomes measured were central corneal thickness (CCT), Iris Vascularity Index (IVI), and Anterior Chamber Particle Index (ACPI). These parameters were assessed using the Swin Transformer V2 artificial intelligence algorithm on AS-OCT images. Results: The study included 180 participants, including uveitis patients and healthy controls. We observed significant differences between these groups in CCT (P = 0.01), ACPI (P < 0.001), and IVI (P < 0.001). Affected eyes showed elevated CCT and ACPI, along with a significant decrease in IVI, especially in severe inflammation cases. Linear regression analysis underscored a robust correlation between these biometric parameters and inflammation severity in the AC (R = 0.481, P < 0.001). A 6-month longitudinal study further validated the stability and repeatability of these measurements, affirming their reliability over time. Conclusions: This study introduces a novel, objective method to quantify ocular inflammation using ACPI, IVI, and CCT, which enhances the precision of assessments over traditional subjective methods prone to interobserver variability. Demonstrated through significant biomarker stability over a 6-month period, our findings support the use of these metrics for reliable long-term monitoring of inflammation progression and treatment efficacy in clinical practice. Translational Relevance: Our artificial intelligence algorithm objectively quantifies AC inflammation reliably over the time and could be used in the clinic as well as in clinical trials as an objective biomarker.eng
dc.format.mimetypeapplication/pdfspa
dc.identifier.doihttps://doi.org/10.1167/tvst.14.5.31spa
dc.identifier.issn2164-2591spa
dc.identifier.urihttps://repository.urosario.edu.co/handle/10336/46094
dc.language.isoengspa
dc.publisherUveitisspa
dc.relation.ispartofTranslational Vision Science & Technology May 2025, Vol. 14, No. 5, Article 31, 2spa
dc.relation.urihttps://tvst.arvojournals.org/article.aspx?articleid=2803032spa
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalspa
dc.rights.accesRightsinfo:eu-repo/semantics/openAccessspa
dc.rights.accesoAbierto (Texto Completo)spa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/spa
dc.sourceTranslational Vision Science & Technologyspa
dc.source.instnameinstname:Universidad del Rosariospa
dc.source.reponamereponame:Repositorio Institucional EdocURspa
dc.subject.keywordUveitiseng
dc.subject.keywordOptical coherence tomographyeng
dc.subject.keywordArtificial intelligenceeng
dc.subject.keywordVision transformereng
dc.subject.keywordIris vascularity indexeng
dc.subject.keywordInflammation measurementeng
dc.titleNovel Artificial Intelligence-Based Quantification of Anterior Chamber Inflammation Using Vision Transformersspa
dc.typearticlespa
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionspa
dc.type.spaArtículo de Investigaciónspa
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