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

Cifuentes-González, C; Gutiérrez-Sinisterra, L; Rojas-Carabali, W; Boon, J; Hudlikar, A; Wei, X; Shchurov, L; Oo, HH; Loh, NC; Shannon, CS; Rodríguez-Camelo, LD; Lee, B; de-la-Torre, A; Agrawal, R

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

Cifuentes-González, C Gutiérrez-Sinisterra, L Rojas-Carabali, W Boon, J Hudlikar, A Wei, X Shchurov, L Oo, HH Loh, NC Shannon, CS Rodríguez-Camelo, LD Lee, B de-la-Torre, A Agrawal, R

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Publisher:: ASSOC RESEARCH VISION OPHTHALMOLOGY INC
Publication Type:: Journal Article
Citation:: Transl Vis Sci Technol, 2025, 14, (5), pp. 31
Issue Date:: 2025-05-01

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Field	Value	Language
dc.contributor.author	Cifuentes-González, C
dc.contributor.author	Gutiérrez-Sinisterra, L
dc.contributor.author	Rojas-Carabali, W
dc.contributor.author	Boon, J
dc.contributor.author	Hudlikar, A
dc.contributor.author	Wei, X
dc.contributor.author	Shchurov, L
dc.contributor.author	Oo, HH
dc.contributor.author	Loh, NC
dc.contributor.author	Shannon, CS
dc.contributor.author	Rodríguez-Camelo, LD
dc.contributor.author	Lee, B
dc.contributor.author	de-la-Torre, A
dc.contributor.author	Agrawal, R
dc.date.accessioned	2026-07-17T00:25:50Z
dc.date.available	2026-07-17T00:25:50Z
dc.date.issued	2025-05-01
dc.identifier.citation	Transl Vis Sci Technol, 2025, 14, (5), pp. 31
dc.identifier.issn	2164-2591
dc.identifier.issn	2164-2591
dc.identifier.uri	http://hdl.handle.net/10453/195694
dc.description.abstract	PURPOSE: 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.
dc.format	Print
dc.language	eng
dc.publisher	ASSOC RESEARCH VISION OPHTHALMOLOGY INC
dc.relation.ispartof	Transl Vis Sci Technol
dc.relation.isbasedon	10.1167/tvst.14.5.31
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0903 Biomedical Engineering, 1113 Opthalmology and Optometry
dc.subject.classification	3212 Ophthalmology and optometry
dc.subject.mesh	Humans
dc.subject.mesh	Cross-Sectional Studies
dc.subject.mesh	Tomography, Optical Coherence
dc.subject.mesh	Male
dc.subject.mesh	Female
dc.subject.mesh	Artificial Intelligence
dc.subject.mesh	Anterior Chamber
dc.subject.mesh	Adult
dc.subject.mesh	Middle Aged
dc.subject.mesh	Inflammation
dc.subject.mesh	Uveitis
dc.subject.mesh	Aged
dc.subject.mesh	Anterior Chamber
dc.subject.mesh	Humans
dc.subject.mesh	Uveitis
dc.subject.mesh	Inflammation
dc.subject.mesh	Tomography, Optical Coherence
dc.subject.mesh	Cross-Sectional Studies
dc.subject.mesh	Artificial Intelligence
dc.subject.mesh	Adult
dc.subject.mesh	Aged
dc.subject.mesh	Middle Aged
dc.subject.mesh	Female
dc.subject.mesh	Male
dc.subject.mesh	Humans
dc.subject.mesh	Cross-Sectional Studies
dc.subject.mesh	Tomography, Optical Coherence
dc.subject.mesh	Male
dc.subject.mesh	Female
dc.subject.mesh	Artificial Intelligence
dc.subject.mesh	Anterior Chamber
dc.subject.mesh	Adult
dc.subject.mesh	Middle Aged
dc.subject.mesh	Inflammation
dc.subject.mesh	Uveitis
dc.subject.mesh	Aged
dc.title	Novel Artificial Intelligence-Based Quantification of Anterior Chamber Inflammation Using Vision Transformers.
dc.type	Journal Article
utslib.citation.volume	14
utslib.location.activity	United States
utslib.for	0903 Biomedical Engineering
utslib.for	1113 Opthalmology and Optometry
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/DVC (Education and Students)
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.date.updated	2026-07-17T00:25:49Z
pubs.issue	5
pubs.publication-status	Published
pubs.volume	14
utslib.citation.issue	5

Abstract:

PURPOSE: 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.

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http://hdl.handle.net/10453/195694