A Deep Learning Approach to Diagnosing Multiple Sclerosis from Smartphone Data
This work addresses the problem of improving MS diagnosis for patients and clinicians by providing an objective, smartphone-based method, though it appears incremental as it builds on existing digital biomarker approaches.
The paper tackled diagnosing multiple sclerosis (MS) using smartphone data, achieving an area under the ROC curve of 0.88 to distinguish between people with and without MS in a cohort of 774 participants.
Multiple sclerosis (MS) affects the central nervous system with a wide range of symptoms. MS can, for example, cause pain, changes in mood and fatigue, and may impair a person's movement, speech and visual functions. Diagnosis of MS typically involves a combination of complex clinical assessments and tests to rule out other diseases with similar symptoms. New technologies, such as smartphone monitoring in free-living conditions, could potentially aid in objectively assessing the symptoms of MS by quantifying symptom presence and intensity over long periods of time. Here, we present a deep-learning approach to diagnosing MS from smartphone-derived digital biomarkers that uses a novel combination of a multilayer perceptron with neural soft attention to improve learning of patterns in long-term smartphone monitoring data. Using data from a cohort of 774 participants, we demonstrate that our deep-learning models are able to distinguish between people with and without MS with an area under the receiver operating characteristic curve of 0.88 (95% CI: 0.70, 0.88). Our experimental results indicate that digital biomarkers derived from smartphone data could in the future be used as additional diagnostic criteria for MS.