ArticleThe ocular surface2023
Automated identification and quantification of activated dendritic cells in central cornea using artificial intelligence.
Article in The ocular surface, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Deep learning-based segmentation and density estimation of corneal nerves and dendritic cells from In Vivo confocal microscopy images.Scientific reports · 2026Article
- AI-driven strategies for advancing corneal cell therapy: a promising frontier.Frontiers in medicine · 2025Review
- Artificial-Intelligence-Enhanced Analysis of In Vivo Confocal Microscopy in Corneal Diseases: A Review.Diagnostics (Basel, Switzerland) · 2024Review
- Artificial Intelligence in Clinical Diagnosis and Treatment of Dry Eye: Workflows, Effectiveness, and Evaluation.Journal of current ophthalmologyReview
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Authors and funding
9 authors.
Funding
Abstract
purposeTo validate an algorithm quantifying activated dendritic cells (aDCs) using in-vivo confocal microscopy (IVCM) images.
methodsIVCM images obtained at the Miami Veterans Affairs Hospital were retrospectively analyzed. ADCs were quantified both with an automated algorithm and manually. Intra-class-correlation (ICC) and a Bland-Altman plot were used to compare automated and manual counts. As a secondary analysis, individuals were grouped by Dry Eye (DE) subtype: 1) aqueous-tear deficiency (ATD; Schirmer's test ≤5 mm); 2) evaporative DE (EDE; TBUT≤5s); or 3) control (Schirmer's test>5 mm; TBUT>5s) and ICCs were re-examined.
results173 non-overlapping images from 86 individuals were included in this study. The mean age was 55.2 ± 16.7 years; 77.9% were male; 20 had ATD; 18 EDE and 37 were controls. The mean number of aDCs in the central cornea quantified automatically was 0.83 ± 1.33 cells/image and manually was 1.03 ± 1.65 cells/image. A total of 143 aDCs were identified by the automated algorithm and 178 aDCs were identified manually. While a Bland-Altman plot indicated a small difference between the two methods (0.19, p < 0.01), the ICC of 0.80 (p = 0.01) demonstrated excellent agreement. Secondarily, similar results were found by DE type with an ICC of 0.75 (p = 0.01) for the ATD group, 0.80 (p = 0.01) for EDE, and 0.82 (p = 0.01) for controls.
conclusionsQuantification of aDCs within the central cornea may be successfully estimated using an automated machine learning based algorithm. While this study suggests that analysis using artificial intelligence has comparable results with manual quantification, further longitudinal research to validate our findings in more diverse populations may be warranted.
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