Artificial intelligence-based screening for amblyopia and its risk factors: comparison with four classic stereovision tests.

Introduction: The development of costs-effective and sensitive screening solutions to prevent amblyopia and identify its risk factors (strabismus, refractive problems or mixed) is a significant priority of pediatric ophthalmology. The main objective of our study was to compare the classification performance of various vision screening tests, including classic, stereoacuity-based tests (Lang II, TNO, Stereo Fly, and Frisby), and non-stereoacuity-based, low-density static, dynamic, and noisy anaglyphic random dot stereograms. We determined whether the combination of non-stereoacuity-based tests integrated in the simplest artificial intelligence (AI) model could be an alternative method for vision screening.

Validation of dynamic random dot stereotests in pediatric vision screening.

Purpose: Stereo vision tests are widely used in the clinical practice for screening amblyopia and amblyogenic conditions. According to literature, none of these tests seems to be suitable to be used alone as a simple and reliable tool. There has been a growing interest in developing new types of stereo vision tests, with sufficient sensitivity to detect amblyopia.