Automated quantification of anterior chamber cells using swept-source anterior segment optical coherence tomography

Shani Pillar; Shin Kadomoto; Keren Chen; Saitiel Sandoval Gonzalez; Nina Cherian; Joseph K Privratsky; Nicolette Zargari; Nicholas J Jackson; Giulia Corradetti; Judy L Chen; SriniVas R Sadda; Gary N Holland; Edmund Tsui

doi:10.1186/s12348-025-00456-y

Automated quantification of anterior chamber cells using swept-source anterior segment optical coherence tomography

J Ophthalmic Inflamm Infect. 2025 Jan 9;15(1):3. doi: 10.1186/s12348-025-00456-y.

Authors

Shani Pillar^{1

2}, Shin Kadomoto³, Keren Chen⁴, Saitiel Sandoval Gonzalez^{1

2}, Nina Cherian^{1

2}, Joseph K Privratsky^{1

2}, Nicolette Zargari^{1

2}, Nicholas J Jackson⁴, Giulia Corradetti⁵, Judy L Chen^{1

2}, SriniVas R Sadda^{2

5}, Gary N Holland^{1

2}, Edmund Tsui^{6

7

8}

Affiliations

¹ Ocular Inflammatory Disease Center, UCLA Jules Stein Eye Institute, Los Angeles, USA.
² Department of Ophthalmology, David Geffen School of Medicine at UCLA, University of California, Los Angeles, Los Angeles, CA, USA.
³ Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan.
⁴ Department of Medicine, Statistics Core, David Geffen School of Medicine at UCLA, University of California, Los Angeles, Los Angeles, CA, USA.
⁵ Doheny Eye Institute, Pasadena, CA, USA.
⁶ Ocular Inflammatory Disease Center, UCLA Jules Stein Eye Institute, Los Angeles, USA. etsui@mednet.ucla.edu.
⁷ Department of Ophthalmology, David Geffen School of Medicine at UCLA, University of California, Los Angeles, Los Angeles, CA, USA. etsui@mednet.ucla.edu.
⁸ UCLA Jules Stein Eye Institute, 100 Stein Plaza, Los Angeles, CA, 90095-7000, USA. etsui@mednet.ucla.edu.

PMID: 39786628
DOI: 10.1186/s12348-025-00456-y

Abstract

Purpose: To validate automated counts of presumed anterior chamber (AC) cells in eyes with histories of uveitis involving the anterior segment using swept-source (SS) anterior segment optical coherence tomography (AS-OCT) against manual counts and compare automated counts against Standardized Uveitis Nomenclature (SUN) criteria.

Methods: Eyes were imaged with the ANTERION SS AS-OCT device (Heidelberg Engineering). A fully automated custom algorithm quantified the number of hyper-reflective foci (HRF) in line-scan images. Automated and manual counts were compared using interclass correlation (ICC) and Pearson correlation coefficient. Automated counts were compared to SUN grades using a mixed-effects linear regression model.

Results: 90 eyes (54 participants) were included; 67 eyes (41 participants) had histories of uveitis, while 23 eyes (13 healthy participants) served as controls. ICC comparing automated to manual counts was 0.99 and the Pearson correlation coefficient was 0.98. Eyes at each SUN grade with corresponding median HRF (interquartile range [IQR]) were: Grade 0, 42 eyes, 2 HRF (0,4); 0.5+, 10 eyes, 10 HRF (8,15); 1+, 9 eyes, 22 HRF (15,33); 2+, 3 eyes, 27 HRF; 3+, 2 eyes, 128 HRF; 4+, 1 eye, 474 HRF. For every 1-step increase in grade, automated count increased by 38 (p < 0.001) or 293% (Pearson correlation coefficient 0.80, p < 0.001). Automated counts differed significantly between clinically inactive eyes (2 HRF [0,4]) and controls (0 HRF [0,1], p = 0.02).

Conclusions: Our algorithm accurately counts HRF when compared to manual counts, with strong correlation to SUN clinical grades. SS AS-OCT offers the advantage of imaging of the entire AC and may allow detection of subclinical inflammation in eyes that appear clinically inactive.

Keywords: Anterior chamber inflammation; Image analysis; Optical coherence tomography (OCT); Standardization of Uveitis nomenclature (SUN); Uveitis.

Grants and funding

K23EY032990/GF/NIH HHS/United States