ArticleOphthalmology science2026
Validation of an Artificial Intelligence-Based Anterior Chamber Depth Estimation Using a Smartphone-Compatible Slit Lamp Device.
Article in Ophthalmology science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 2 papers.
What it found
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Development of a Deep Learning Model to Estimate Anemia from Palpebral Conjunctiva Taken with a Portable Slit-Lamp Microscope.Bioengineering (Basel, Switzerland) · 2026Article
- Prospective Evaluation of Ocular Anterior Segment Morphology Changes in the Steep Trendelenburg Position During Robotic-Assisted Laparoscopic Prostatectomy.Journal of clinical medicine · 2026Article
Corrections and comments
- Erratum issuedErrata.2026
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Accurate evaluation of anterior chamber depth (ACD), a major risk factor for angle closure, is clinically important. Although standard techniques provide reliable measurements, they are often labor-intensive, technically demanding, and time-consuming. To address this, we previously developed an artificial intelligence (AI) algorithm capable of estimating ACD from slit lamp photographs. This study sought to assess the performance of this AI model when applied via the Smart Eye Camera (SEC), a smartphone-compatible slit lamp imaging system, by comparing its estimates to those obtained with anterior segment OCT (AS-OCT) at a separate institution. Design: An evaluation of diagnostic test. Participants: Five hundred fifty-six phakic eyes (268 nondilated and 288 dilated eyes) from 329 Asian patients. Methods: A retrospective analysis was performed on images captured using both the SEC and AS-OCT. Anterior chamber depth values generated by the AI model embedded in the SEC were compared with corresponding measurements obtained using AS-OCT. Main Outcome Measures: Metrics, including mean absolute error (MAE), mean squared error (MSE), Pearson correlation coefficient, and the intraclass correlation coefficient (ICC), were calculated to evaluate model performance. Results: The AI algorithm integrated into the SEC demonstrated the capability to estimate the ACD with an MAE of 0.119 ± 0.0949 mm and an MSE of 0.0233 ± 0.0557 mm. A strong correlation was observed between ACD measurements obtained using AS-OCT and those estimated by AI ( Conclusions: Our AI model, embedded within the SEC platform, demonstrated high accuracy in estimating ACD when benchmarked against AS-OCT. Given its portability and user-friendliness, the SEC presents a promising option for accessible ACD screening across diverse clinical environments. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.