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Updated: Jul 13, 2026
Smartphone Fundus Photography
Published on: July 6, 2017
Validation of an Artificial Intelligence-Based Anterior Chamber Depth Estimation Using a Smartphone-Compatible Slit
Takahiro Mizukami1, Eisuke Shimizu2,3, Kenta Tanaka2
1Department of Ophthalmology, Fuchu Hospital, Izumi, Osaka, Japan.
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 (R = 0.922; 95% confidence interval, 0.908-0.933). The ICC for agreement between AI-estimated and AS-OCT-measured ACD was 0.903, indicating excellent reliability.
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.
