A preliminary exploration of quantitative models for evaluating binocular visual perception impairments among...

The objective of this study is to formulate and implement graded biological models pertaining to binocular visual perception function with the use of computer algorithms. We aim to quantitatively assess the location, severity, and degree of impairment in binocular visual perception among patients who have suffered stroke, thereby providing valuable insights into the repercussions of cerebral tissue damage on the visual system. To overcome the shortcomings of previous instruments used to assess binocular function in terms of stereoscopic effects and the challenges posed by physiological and psychological interference during examinations, this study optimized its approach by integrating polarized stereovision technology with computer graphic modeling techniques. This study employed computer models to assess binocular visual perception function in stroke patients. Computer models refer to psychophysical testing methods used to measure binocular visual perception function, including various assessment tasks such as recognizing inverted letters and assessing stereopsis during high-speed movements. The cross-into-circle test was used as a means to quantify perceptual eye position. Subsequently, the collected data was analyzed to assess the magnitude of impairment in binocular visual perception. The results of the study revealed a spectrum of binocular visual perception impairment among patients diagnosed with stroke, demonstrating discernible variations in the recognition of inverted letters and stereopsis across different movement speeds. Importantly, perceptual eye position measurements offered valuable insights into ocular misalignment. The computational models effectively quantified both the spatial distribution and severity of these identified impairments. Damage to brain tissue resulting from a stroke can give rise to notable impairments in binocular visual perception function. Graded biological models, formulated through computer algorithms, provide a systematic framework for the comprehensive evaluation and quantification of these impairments. The comprehension of the nature and extent of visual impairments observed in patients with stroke establishes a basis for the development of personalized visual perception learning methodologies. Based on such tailored approaches, we aim to facilitate the recovery of impaired visual function, thereby contributing to the broader objective of neural system rehabilitation.