Quantification of Parkinsonian unilateral involvement from ocular fixational patterns using a deep video representation

Abstract Purpose Lateralisation of motor symptoms is a prevalent characteristic of Parkinson’s disease (PD). Hence, unilateral involvement is crucial for personalized treatments and measuring therapy effectiveness. Nonetheless, most motor symptoms, including lateralization, are mainly evident at advanced stages of the disease. Recently, ocular fixation instability emerged as a promising PD biomarker with a high sensitivity to discriminate PD. We hypothesize that unilateral involvement can be recovered from the assessment and quantification of PD-related ocular abnormalities. Methods This method proposes a computer-based strategy to quantify PD lateralization from ocular fixation patterns. The method follows a markerless strategy fed by slices with spatiotemporal eye movement information. A deep convolutional model was used to discriminate between PD and a control population. Additionally, model prediction probabilities were analyzed to select the dominant eye associated with unilateral involvement. Results The proposed approach reports an average accuracy of 91.92% classifying PD. Interestingly, using the dominant side, the approach achieves an average PD prediction probability of 93.3% (95% CI: [91.61,95.07]), evidencing capabilities to capture the most affected side. Besides, the reported results strongly correlate with the disease, even for patients categorized at early stages. A low-dimensional projection tool was used to support the classification results by finding a 2d space that eases the discrimination among classes. Conclusions The strategy is sensitive to detecting and classifying PD fixational patterns and determining the side with major impairments. This approach may be a potential tool to support the characterization of the disease and as an alternative to defining personalized treatments.