Rapid measurement and machine learning classification of colour vision deficiency

Colour 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 tests which possess high sensitivity for detecting the presence of a CVD but do not quantify its type or severity. In this study, we introduce Foraging Interactive D-prime (FInD), a novel computer-based, generalisable, rapid, self-administered vision assessment tool and apply it to colour vision testing. This signal detection theory-based adaptive paradigm computed test stimulus intensity from d-prime analysis. Stimuli were chromatic Gaussian blobs in dynamic luminance noise, and participants clicked on cells that contained chromatic blobs (detection) or blob pairs of differing colours (discrimination). Sensitivity and repeatability of FInD colour tasks were compared against the Hardy-Rand-Rittler and the Farnsworth-Munsell 100 hue tests in 19 colour-normal and 18 inherited colour-atypical, age-matched observers. Rayleigh colour match was also completed. Detection and discrimination thresholds were higher for atypical 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 inherited CVDs, and may serve as valuable tools in basic and clinical colour vision science.