Automated pupillometry is an emerging non-invasive tool for screening, diagnosis, and monitoring of ocular diseases. From biomedical engineering perspective, identifying the most useful biomarkers of the pupillary light reflex and post-illumination pupil responses is important in the automated pupillometry device design. However, existing reviews lack cross-disease systemic comparisons and parameter-stimulus mapping. This systematic review aims to evaluate the use of pupillometric features of the five most prevalent eye conditions: glaucoma, diabetic retinopathy, cataracts, macular degeneration, and refractive errors. Key findings suggest that glaucoma can be detected by dichoptic stimulation and binocular recording to measure Relative Afferent Pupillary Defect. Diabetic retinopathy shows focal stimulation asymmetry between central and peripheral regions. In macular degeneration, focal foveal stimulation through fixation tracking and continuous analysis during phasic red and blue light stimulation can differentiate affected eyes. Refractive error studies suggest predictive potential, particularly using alternating red and blue wavelength stimuli, and comparing to corresponding time points with single colour stimuli. This review establishes a specification framework for detectable biomarkers and device capabilities required of an automated pupilometer to be most useful in early detection, including focal and full-field stimulation, binocular recording, alternating wavelength delivery, and real-time analysis.

Automated pupillometry is an emerging non-invasive tool for screening, diagnosis, and monitoring of ocular diseases. From biomedical engineering perspective, identifying the most useful biomarkers of the pupillary light reflex and post-illumination pupil responses is important in the automated pupillometry device design. This systematic review aims to evaluate the use of pupillometric features of the five most prevalent eye conditions: glaucoma, diabetic retinopathy, cataracts, macular degeneration, and refractive errors. Results were used to identify hardware and software requirements to specify the stimulation and analysis capabilities for early detection. Following PRISMA guidelines, a systematic search was conducted in Scopus database and studies were appraised through MMAT tool. Sixty-one studies were included, with findings synthesized by disease and stimulation protocol and interpreted by biomedical relevance and technical feasibility. Key findings suggest that glaucoma can be detected by binocular stimulation and recording to measure Relative Afferent Pupillary Defect. Diabetic retinopathy shows focal stimulation asymmetry between central and peripheral regions. In macular degeneration, focal foveal stimulation through fixation tracking and continuous analysis during phasic red and blue light stimulation can differentiate affected eyes. Refractive error studies suggest predictive potential, particularly using alternating red and blue wavelength stimuli, and comparing to corresponding time points with single colour stimuli. This review establishes a specification framework for detectable biomarkers and device capabilities required of an automated pupilometer to be most useful in early detection, including focal and full-field stimulation, binocular recording, alternating wavelength delivery, and real-time analysis.