Diagnosis of colour vision deficits using eye movements

Digital biomarkers for non-motor symptoms in Parkinson's disease: the state of the art

Digital biomarkers that remotely monitor symptoms have the potential to revolutionize outcome assessments in future disease-modifying trials in Parkinson's disease (PD), by allowing objective and recurrent measurement of symptoms and signs collected in the participant's own living environment. This biomarker field is developing rapidly for assessing the motor features of PD, but the non-motor domain lags behind. Here, we systematically review and assess digital biomarkers under development for measuring non-motor symptoms of PD. We also consider relevant developments outside the PD field. We focus on technological readiness level and evaluate whether the identified digital non-motor biomarkers have potential for measuring disease progression, covering the spectrum from prodromal to advanced disease stages. Furthermore, we provide perspectives for future deployment of these biomarkers in trials. We found that various wearables show high promise for measuring autonomic function, constipation and sleep characteristics, including REM sleep behavior disorder. Biomarkers for neuropsychiatric symptoms are less well-developed, but show increasing accuracy in non-PD populations. Most biomarkers have not been validated for specific use in PD, and their sensitivity to capture disease progression remains untested for prodromal PD where the need for digital progression biomarkers is greatest. External validation in real-world environments and large longitudinal cohorts remains necessary for integrating non-motor biomarkers into research, and ultimately also into daily clinical practice.

(The limits of) eye-tracking with iPads

Applications for eye-tracking-particularly in the clinic-are limited by a reliance on dedicated hardware. Here we compare eye-tracking implemented on an Apple iPad Pro 11" (third generation)-using the device's infrared head-tracking and front-facing camera-with a Tobii 4c infrared eye-tracker. We estimated gaze location using both systems while 28 observers performed a variety of tasks. For estimating fixation, gaze position estimates from the iPad were less accurate and precise than the Tobii (mean absolute error of 3.2° ± 2.0° compared with 0.75° ± 0.43°), but fixation stability estimates were correlated across devices (r = 0.44, p < 0.05). For tasks eliciting saccades >1.5°, estimated saccade counts (r = 0.4-0.73, all p < 0.05) were moderately correlated across devices. For tasks eliciting saccades >8° we observed moderate correlations in estimated saccade speed and amplitude (r = 0.4-0.53, all p < 0.05). We did, however, note considerable variation in the vertical component of estimated smooth pursuit speed from the iPad and a catastrophic failure of tracking on the iPad in 5% to 20% of observers (depending on the test). Our findings sound a note of caution to researchers seeking to use iPads for eye-tracking and emphasize the need to properly examine their eye-tracking data to remove artifacts and outliers.

Eye Tracking in Optometry: A Systematic Review

This systematic review examines the use of eye-tracking devices in optometry, describing their main characteristics, areas of application and metrics used. Using the PRISMA method, a systematic search was performed of three databases. The search strategy identified 141 reports relevant to this topic, indicating the exponential growth over the past ten years of the use of eye trackers in optometry. Eye-tracking technology was applied in at least 12 areas of the field of optometry and rehabilitation, the main ones being optometric device technology, and the assessment, treatment, and analysis of ocular disorders. The main devices reported on were infrared light-based and had an image capture frequency of 60 Hz to 2000 Hz. The main metrics mentioned were fixations, saccadic movements, smooth pursuit, microsaccades, and pupil variables. Study quality was sometimes limited in that incomplete information was provided regarding the devices used, the study design, the methods used, participants' visual function and statistical treatment of data. While there is still a need for more research in this area, eye-tracking devices should be more actively incorporated as a useful tool with both clinical and research applications. This review highlights the robustness this technology offers to obtain objective information about a person's vision in terms of optometry and visual function, with implications for improving visual health services and our understanding of the vision process.