Eye-Movement–Based Assessment of the Perceptual Consequences of Glaucomatous and Neuro-Ophthalmological Visual Field Defects

Abstract

Purpose Assessing the presence of visual field defects (VFD) through procedures such as perimetry is an essential aspect of the management and diagnosis of ocular disorders. However, even the latest perimetric methods have shortcomings—a high cognitive demand and requiring prolonged stable fixation and feedback through a button response. Consequently, an approach using eye movements (EM)—as a natural response—has been proposed as an alternate way to evaluate the presence of VFD. This approach has given good results for computer-simulated VFD. However, its use in patients is not well documented yet. Here we use this new approach to quantify the spatiotemporal properties (STP) of EM of various patients suffering from glaucoma and neuro-ophthalmological VFD and controls. Methods In total, 15 glaucoma patients, 37 patients with a neuro-ophthalmological disorder, and 21 controls performed a visual tracking task while their EM were being recorded. Subsequently, the STP of EM were quantified using a cross-correlogram analysis. Decision trees were used to identify the relevant STP and classify the populations. Results We achieved a classification accuracy of 94.5% (TPR/sensitivity = 96%, TNR/specificity = 90%) between patients and controls. Individually, the algorithm achieved an accuracy of 86.3% (TPR for neuro-ophthalmology [97%], glaucoma [60%], and controls [86%]). The STP of EM were highly similar across two different control cohorts. Conclusions In an ocular tracking task, patients with VFD due to different underlying pathology make EM with distinctive STP. These properties are interpretable based on different clinical characteristics of patients and can be used for patient classification. Translational Relevance Our EM-based screening tool may complement existing perimetric techniques in clinical practice.

Patients Prefer a Virtual Reality Approach Over a Similarly Performing Screen-Based Approach for Continuous Oculomotor-Based Screening of Glaucomatous and Neuro-Ophthalmological Visual Field Defects

“…Central and peripheral defects differentially affect this. Using simulations (Grillini et al, 2018) and measurements in actual patients (Soans et al, 2021), we have shown that the method works very well (VFD simulations: Accuracy = 90%, TPR = 98%; Patients: Accuracy = 94.5%, TPR = 96%). However, our new method still has some aspects that may limit its use.…”

“…In both the VR and screen-based eyetracker, we first obtain the gaze positions in terms of screen coordinates which is then converted into visual field coordinates. Subsequently, the gaze data is corrected for blinks according to a custom algorithm (Soans et al, 2021; see Supplementary Material for description). Owing to differences in the sampling rate and the physical screen size of the devices, we use empirically determined spike thresholds of 60 • /s and 190 • /s in the vertical gaze velocity components of the FOVE and the Tobii gaze data, respectively.…”

“…These properties are reflective of temporal correlation between the stimuli and response, smooth pursuit latency, temporal uncertainty, and resemblance of the tracking performance to a Gaussian distribution, respectively. (2) Spatial properties (Grillini et al, 2018;Soans et al, 2021): These include positional error distributions (PEDs) that yield additional four properties (PED Amplitude, PED Mean, PED Standard Deviation, and PED variance explained) for the horizontal and vertical components of the eye positions, respectively. These properties indicate the most frequently observed positional error, spatial offset, spatial uncertainty, and the resemblance of the PED to a Gaussian distribution, respectively.…”

“…These properties indicate the most frequently observed positional error, spatial offset, spatial uncertainty, and the resemblance of the PED to a Gaussian distribution, respectively. (3) Integrated properties (Grillini et al, 2018;Soans et al, 2021): These two properties (Cosine similarity and Observation noise variance) incorporate both the spatial deviations and the temporal delays in the stimulus and response time series.…”

“…These issues render SAP ineffective in such groups and interfere with diagnosing the ocular disorder. Therefore, our group developed and clinically tested a novel approach (Grillini et al, 2018;Soans et al, 2021) to screen for visual field defects (VFDs) based on eye movements (EMs).…”