Rapid measurement and machine learning classification of color vision deficiency

Color vision deficiencies (CVDs) indicate potential genetic variations and can be important biomarkers of acquired impairment in many neuro-ophthalmic diseases. However, CVDs are typically measured with insensitive or inefficient tools that are designed to classify dichromacy subtypes rather than track changes in sensitivity. We introduce FInD (Foraging Interactive D-prime), a novel computer-based, generalizable, rapid, self-administered vision assessment tool and applied it to color vision testing. This signal detection theory-based adaptive paradigm computes test stimulus intensity from d-prime analysis. Stimuli were chromatic gaussian blobs in dynamic luminance noise, and participants clicked on cells that contain chromatic blobs (detection) or blob pairs of differing colors (discrimination). Sensitivity and repeatability of FInD Color tasks were compared against HRR, FM100 hue tests in 19 color-normal and 18 color-atypical, age-matched observers. Rayleigh color match was completed as well. Detection and Discrimination thresholds were higher for atypical observers than for typical observers, with selective threshold elevations corresponding to unique CVD types. Classifications of CVD type and severity via unsupervised machine learning confirmed functional subtypes. FInD tasks reliably detect CVD and may serve as valuable tools in basic and clinical color vision science.