Automatic identification of tinnitus malingering based on overt and covert behavioral responses during psychoacoustic testing

Tinnitus, or ringing in the ears, is a prevalent condition that imposes a substantial health and financial burden on the patient and to society. The diagnosis of tinnitus, like pain, relies on patient self-report, which can complicate the distinction between actual and fraudulent claims. Here, we combined tablet-based self-directed hearing assessments with neural network classifiers to automatically differentiate participants with tinnitus (N = 24) from a malingering cohort, who were instructed to feign an imagined tinnitus percept (N = 28). We identified clear differences between the groups, both in their overt reporting of tinnitus features, but also covert differences in their fingertip movement trajectories on the tablet surface as they performed the reporting assay. Using only 10 min of data, we achieved 81% accuracy classifying patients and malingerers (ROC AUC = 0.88) with leave-one-out cross validation. Quantitative, automated measurements of tinnitus salience could improve clinical outcome assays and more accurately determine tinnitus incidence.