Automated quantification of anterior chamber cells using swept-source anterior segment optical coherence tomography
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 Standardize...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-01-01
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| Series: | Journal of Ophthalmic Inflammation and Infection |
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| Online Access: | https://doi.org/10.1186/s12348-025-00456-y |
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| author | 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 |
| author_facet | 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 |
| author_sort | Shani Pillar |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2e042883a024447ab8f2c509addd2232 |
| institution | Kabale University |
| issn | 1869-5760 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Ophthalmic Inflammation and Infection |
| spelling | doaj-art-2e042883a024447ab8f2c509addd22322025-01-12T12:35:20ZengSpringerOpenJournal of Ophthalmic Inflammation and Infection1869-57602025-01-011511710.1186/s12348-025-00456-yAutomated quantification of anterior chamber cells using swept-source anterior segment optical coherence tomographyShani Pillar0Shin Kadomoto1Keren Chen2Saitiel Sandoval Gonzalez3Nina Cherian4Joseph K. Privratsky5Nicolette Zargari6Nicholas J. Jackson7Giulia Corradetti8Judy L. Chen9SriniVas R. Sadda10Gary N. Holland11Edmund Tsui12Ocular Inflammatory Disease Center, UCLA Jules Stein Eye InstituteDepartment of Ophthalmology and Visual Sciences, Kyoto University Graduate School of MedicineDepartment of Medicine, Statistics Core, David Geffen School of Medicine at UCLA, University of California, Los AngelesOcular Inflammatory Disease Center, UCLA Jules Stein Eye InstituteOcular Inflammatory Disease Center, UCLA Jules Stein Eye InstituteOcular Inflammatory Disease Center, UCLA Jules Stein Eye InstituteOcular Inflammatory Disease Center, UCLA Jules Stein Eye InstituteDepartment of Medicine, Statistics Core, David Geffen School of Medicine at UCLA, University of California, Los AngelesDoheny Eye InstituteOcular Inflammatory Disease Center, UCLA Jules Stein Eye InstituteDepartment of Ophthalmology, David Geffen School of Medicine at UCLA, University of California, Los AngelesOcular Inflammatory Disease Center, UCLA Jules Stein Eye InstituteOcular Inflammatory Disease Center, UCLA Jules Stein Eye InstituteAbstract 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.https://doi.org/10.1186/s12348-025-00456-yUveitisAnterior chamber inflammationOptical coherence tomography (OCT)Image analysisStandardization of Uveitis nomenclature (SUN) |
| spellingShingle | 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 Automated quantification of anterior chamber cells using swept-source anterior segment optical coherence tomography Journal of Ophthalmic Inflammation and Infection Uveitis Anterior chamber inflammation Optical coherence tomography (OCT) Image analysis Standardization of Uveitis nomenclature (SUN) |
| title | Automated quantification of anterior chamber cells using swept-source anterior segment optical coherence tomography |
| title_full | Automated quantification of anterior chamber cells using swept-source anterior segment optical coherence tomography |
| title_fullStr | Automated quantification of anterior chamber cells using swept-source anterior segment optical coherence tomography |
| title_full_unstemmed | Automated quantification of anterior chamber cells using swept-source anterior segment optical coherence tomography |
| title_short | Automated quantification of anterior chamber cells using swept-source anterior segment optical coherence tomography |
| title_sort | automated quantification of anterior chamber cells using swept source anterior segment optical coherence tomography |
| topic | Uveitis Anterior chamber inflammation Optical coherence tomography (OCT) Image analysis Standardization of Uveitis nomenclature (SUN) |
| url | https://doi.org/10.1186/s12348-025-00456-y |
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