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Diopter measurement based on an artificial neural network.

Tao Jin, Xuan Gao

|February 24, 2022

Summary

This study introduces an artificial neural network for measuring spectacle lens diopters. The radial basis function (RBF) neural network demonstrated superior stability and accuracy compared to the backpropagation (BP) network, overcoming auto-focimeter errors.

Area of Science:

Optometry
Artificial Intelligence
Optical Engineering

Background:

Accurate measurement of spectacle lens diopters is crucial for vision correction.
Traditional methods like auto-focimeters can be susceptible to system errors, particularly with varying lens curvature.
Artificial neural networks offer a potential solution for enhanced precision in optical measurements.

Purpose of the Study:

To propose and evaluate an artificial neural network-based method for measuring spectacle lens diopters.
To compare the performance of radial basis function (RBF) and backpropagation (BP) neural networks for diopter measurement.
To assess the ability of the proposed method to overcome system errors associated with lens curvature.

Main Methods:

Utilized the Hartmann test to obtain spot distances imaged by a charge-coupled device (CCD).

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Trained a backpropagation (BP) neural network and a radial basis function (RBF) neural network using the Hartmann test data.

Tested the method with spectacle lenses ranging from -20D to 20D.

Main Results:

The RBF neural network exhibited higher stability and lower measurement errors compared to the BP neural network.
The RBF neural network's diopter measurements were not adversely affected by the system error observed in auto-focimeters due to changes in lens curvature.
The proposed RBF neural network method demonstrated superior performance in diopter detection.

Conclusions:

The radial basis function (RBF) neural network is a highly effective tool for accurate spectacle lens diopter measurement.
This AI-driven approach overcomes limitations of conventional auto-focimeters, particularly concerning lens curvature variations.
The findings suggest a promising advancement in ophthalmic lens metrology using artificial intelligence.