Rijul Saurabh Soans1,2, Alessandro Grillini2, Rohit Saxena3, Remco J Renken4, Tapan Kumar Gandhi1, Frans W Cornelissen2. 1. Department of Electrical Engineering, Indian Institute of Technology - Delhi, New Delhi, India. 2. Laboratory of Experimental Ophthalmology, University Medical Center Groningen, University of Groningen, The Netherlands. 3. Department of Ophthalmology, Dr. Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences, New Delhi, India. 4. Cognitive Neuroscience Center, Department of Biomedical Sciences of Cells and Systems, University Medical Center Groningen, University of Groningen, The Netherlands.
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.
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 glaucomapatients, 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.
Authors: Francesca D'Addio; Ida Pastore; Cristian Loretelli; Alessandro Valderrama-Vasquez; Vera Usuelli; Emma Assi; Chiara Mameli; Maddalena Macedoni; Anna Maestroni; Antonio Rossi; Maria Elena Lunati; Paola Silvia Morpurgo; Alessandra Gandolfi; Laura Montefusco; Andrea Mario Bolla; Moufida Ben Nasr; Stefania Di Maggio; Lisa Melzi; Giovanni Staurenghi; Antonio Secchi; Stefania Bianchi Marzoli; Gianvincenzo Zuccotti; Paolo Fiorina Journal: Acta Diabetol Date: 2022-06-22 Impact factor: 4.087