Depression is the most prevalent mental disorder in the world. One of the most adopted tools for depression screening is the Beck Depression Inventory-II (BDI-II) questionnaire. Patients may minimize or exaggerate their answers. Thus, to further examine the patient’s mood while filling in the questionnaire, we propose a mobile application that captures the BDI-II patient’s responses together with their images and speech. Deep learning techniques such as Convolutional Neural Networks analyze the patient’s audio and image data. The application displays the correlation between the patient’s emotional scores and DBI-II scores to the clinician at the end of the questionnaire, indicating the relationship between the patient’s emotional state and the depression screening score. We conducted a preliminary evaluation involving clinicians and patients to assess (i) the acceptability of proposed application for use in clinics and (ii) the patient user experience. The participants were eight clinicians who tried the tool with 21 of their patients. The results seem to confirm the acceptability of the app in clinical practice.

Emotion detection for supporting depression screening

Francese R.;Attanasio P.
2023-01-01

Abstract

Depression is the most prevalent mental disorder in the world. One of the most adopted tools for depression screening is the Beck Depression Inventory-II (BDI-II) questionnaire. Patients may minimize or exaggerate their answers. Thus, to further examine the patient’s mood while filling in the questionnaire, we propose a mobile application that captures the BDI-II patient’s responses together with their images and speech. Deep learning techniques such as Convolutional Neural Networks analyze the patient’s audio and image data. The application displays the correlation between the patient’s emotional scores and DBI-II scores to the clinician at the end of the questionnaire, indicating the relationship between the patient’s emotional state and the depression screening score. We conducted a preliminary evaluation involving clinicians and patients to assess (i) the acceptability of proposed application for use in clinics and (ii) the patient user experience. The participants were eight clinicians who tried the tool with 21 of their patients. The results seem to confirm the acceptability of the app in clinical practice.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4841091
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