Abstract
PURPOSE: Stereopsis, the ability of humans to perceive depth through distinct visual stimuli in each eye, is foundational to autostereoscopic technology in computing. However, ensuring stable head position during assessments has been challenging. This study evaluated the utility of artificial intelligence (AI)-enhanced face tracking technology in overcoming this challenge by ensuring that each eye consistently receives its intended image. METHODS: The Lume Pad 2, an autostereoscopic tablet with AI-enhanced face tracking, was utilized to simulate quantitative parts of the Stereo Fly test and TNO Stereotests for contour and random dot stereopsis. The study recruited 30 children (14 males and 16 females, mean age of 9.2 ± 0.3 years, age range of 6-12 years) and 30 adults (10 males and 20 females, mean age of 29.4 ± 1.0 years, age range of 21-42 years) to assess the tablet's inter-session reliability. Agreement between conventional and the autostereoscopic tablet-simulated stereotests was tested in a larger group of 181 children (91 males and 90 females, mean age of 9.1 ± 0.4 years, age range of 6-12 years) and 160 adults (69 males and 91 females, mean age of 38.6 ± 2.1 years, age range of 21-65 years). Inter-session reliability and agreement were analyzed using weighted Kappa coefficient and non-parametric Bland-Altman analysis. RESULTS: The autostereoscopic tablet demonstrated high inter-session reliability (κ all > 0.80), except for the simulated TNO Stereotest in adults, which demonstrated moderate inter-session reliability (κ = 0.571). Non-parametric Bland-Altman analysis revealed zero median differences, confirming consistent inter-session reliability. Similar patterns were observed in comparing AI-based and conventional methods, with both the weighted Kappa coefficient (κ all > 0.80) and non-parametric Bland-Altman analysis indicating significant agreement. The agreement between methodologies was confirmed by permissible differences, which were smaller than the minimum step range. CONCLUSION: The integration of AI-based autostereoscopic technology with sub-pixel precision demonstrates significant potential for clinical stereopsis measurements.