A digital eye-fixation biomarker using a deep anomaly scheme to classify Parkisonian patterns
This provides a potential digital biomarker for Parkinson's disease detection, particularly in early stages, but is incremental as it builds on existing video analysis methods with a one-class learning twist.
The authors tackled the problem of detecting Parkinson's disease using eye movement patterns by developing a deep anomaly detection scheme that avoids the need for large, balanced datasets, achieving an AUC-ROC of 0.95 with sensitivity of 0.97 and specificity of 0.63 in a study with 13 patients and 13 controls.
Oculomotor alterations constitute a promising biomarker to detect and characterize Parkinson's disease (PD), even in prodromal stages. Currently, only global and simplified eye movement trajectories are employed to approximate the complex and hidden kinematic relationships of the oculomotor function. Recent advances on machine learning and video analysis have encouraged novel characterizations of eye movement patterns to quantify PD. These schemes enable the identification of spatiotemporal segments primarily associated with PD. However, they rely on discriminative models that require large training datasets and depend on balanced class distributions. This work introduces a novel video analysis scheme to quantify Parkinsonian eye fixation patterns with an anomaly detection framework. Contrary to classical deep discriminative schemes that learn differences among labeled classes, the proposed approach is focused on one-class learning, avoiding the necessity of a significant amount of data. The proposed approach focuses only on Parkinson's representation, considering any other class sample as an anomaly of the distribution. This approach was evaluated for an ocular fixation task, in a total of 13 control subjects and 13 patients on different stages of the disease. The proposed digital biomarker achieved an average sensitivity and specificity of 0.97 and 0.63, respectively, yielding an AUC-ROC of 0.95. A statistical test shows significant differences (p < 0.05) among predicted classes, evidencing a discrimination between patients and control subjects.