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Light Acquisition02:16

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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.

Ophthalmology Science
|September 29, 2025
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Summary

An artificial intelligence (AI) algorithm estimates anterior chamber depth (ACD) using the Smart Eye Camera (SEC). This portable system shows high accuracy, comparable to established methods, for accessible glaucoma screening.

Keywords:
Angle-closure glaucomaAnterior chamber depthAnterior-segment OCTArtificial intelligenceSmart Eye Camera

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Accurate anterior chamber depth (ACD) measurement is crucial for identifying glaucoma risk.
  • Traditional methods for ACD measurement are time-consuming and technically demanding.
  • An AI algorithm was previously developed to estimate ACD from slit lamp images.

Purpose of the Study:

  • To evaluate the performance of a previously developed AI algorithm for ACD estimation.
  • To assess the AI model's accuracy when used with the smartphone-compatible Smart Eye Camera (SEC).
  • To compare AI-driven ACD estimates from the SEC against gold-standard anterior segment OCT (AS-OCT) measurements.

Main Methods:

  • A retrospective analysis of images from 556 phakic eyes of 329 Asian patients was conducted.
  • Anterior chamber depth (ACD) was measured using both the SEC with embedded AI and AS-OCT.
  • Performance metrics included mean absolute error (MAE), mean squared error (MSE), Pearson correlation, and intraclass correlation coefficient (ICC).

Main Results:

  • The AI algorithm integrated into the SEC achieved a mean absolute error (MAE) of 0.119 ± 0.0949 mm.
  • A strong correlation (R = 0.922) was found between AI-estimated ACD and AS-OCT measurements.
  • The intraclass correlation coefficient (ICC) of 0.903 indicated excellent agreement between the AI and AS-OCT methods.

Conclusions:

  • The AI model within the SEC platform accurately estimates anterior chamber depth (ACD).
  • The SEC offers a portable and user-friendly tool for ACD screening.
  • This technology shows promise for widespread, accessible glaucoma risk assessment in various clinical settings.