Eye-Movement-Based Assessment of the Perceptual Consequences of Glaucomatous and Neuro-Ophthalmological Visual Field Defects
Autor: | Tapan K. Gandhi, Rijul Saurabh Soans, Frans W. Cornelissen, Rohit Saxena, Remco J. Renken, Alessandro Grillini |
---|---|
Přispěvatelé: | Clinical Cognitive Neuropsychiatry Research Program (CCNP), Perceptual and Cognitive Neuroscience (PCN) |
Jazyk: | angličtina |
Rok vydání: | 2021 |
Předmět: |
medicine.medical_specialty
genetic structures media_common.quotation_subject Biomedical Engineering Decision tree Vision Disorders Glaucoma Audiology eye tracking Article Neuro-ophthalmology perimetry Perception medicine Humans media_common neuro-ophthalmology decision trees business.industry screening Eye movement Cognition visual field defects cross correlogram medicine.disease eye diseases Visual field Ophthalmology eye movements glaucoma Eye tracking Visual Field Tests Visual Fields business |
Zdroj: | Translational Vision Science & Technology Translational Vision Science & Technology, 10(2). Association for Research in Vision and Ophthalmology (ARVO) |
ISSN: | 2164-2591 |
Popis: | 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 & mdash;a high cognitive demand and requiring prolonged stable fixation and feedback through a button response. Consequently, an approach using eye movements (EM)& mdash;as a natural response & mdash;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. Superscript/Subscript Available 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. |
Databáze: | OpenAIRE |
Externí odkaz: |