Feasibility of deep learning to predict tinnitus patient outcomes

Abstract

Advances in machine and deep learning techniques provide a novel approach in understanding complex patterns within large datasets, leading to an implementation of personalized medicine approaches to support clinical decision making. Results from recent clinical trials (TENT-A1 and TENT-A2 studies; clinicaltrials.gov: NCT02669069 and NCT03530306) support that a novel bimodal neuromodulation approach could be a breakthrough treatment for patients with tinnitus, which adversely affects 10–15 % of the population. Given the heterogeneity of symptoms, it is important to identify whether treatment has an optimal effect on specific subgroups of tinnitus patients. The current study is a first look at the feasibility of using deep learning modelling on patient reported data to predict treatment outcomes in individuals with tinnitus, and highlights what features are most beneficial for clinical decision making.