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Depression is a prevalent mental illness marked by persistent sadness and lack of interest in previously enjoyable activities. It can take several forms, including major depression, persistent depressive disorder, and bipolar I and II disorders. Symptoms range from emotional changes like chronic worry to physical changes like sleep disturbances and suicidal thoughts. From a neurobiological perspective, depression is believed to be triggered by abnormalities in the brain's prefrontal cortex,...
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RADIANCE: Reliable and interpretable depression detection from speech using transformer.

Anup Kumar Gupta1, Ashutosh Dhamaniya1, Puneet Gupta1

  • 1Department of Computer Science and Engineering, Indian Institute of Technology Indore, Indore, 452020, Madhya Pradesh, India.

Computers in Biology and Medicine
|November 3, 2024
PubMed
Summary
This summary is machine-generated.

A new method called RADIANCE (Reliable AnD InterpretAble depressioN deteCtion transformErs) uses speech to reliably detect depression. It offers interpretable results and outperforms existing methods in accuracy.

Keywords:
Audio modalityDepression detectionInterpretabilityTrustworthinessVision transformer

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Area of Science:

  • Computational Psychiatry
  • Artificial Intelligence in Healthcare
  • Speech Signal Processing

Background:

  • Depression is a prevalent mental disorder significantly impacting daily functioning.
  • Many cases remain undiagnosed due to stigma and limited healthcare access.
  • Existing deep learning models for speech-based depression detection lack transparency.

Purpose of the Study:

  • To develop a reliable and interpretable deep learning model for automatic depression detection using speech.
  • To address the limitations of black-box models in clinical settings.
  • To improve the accuracy and trustworthiness of AI-driven mental health assessments.

Main Methods:

  • Introduction of RADIANCE (Reliable AnD InterpretAble depressioN deteCtion transformErs), featuring a FilterBank Vision Transformer (FBViT) for interpretable symptom identification.
  • Implementation of a novel loss function to manage class imbalance and misclassification hierarchies.
  • Development of a reliability predictor using low-level descriptors to quantify prediction trustworthiness and enhance multi-clip audio analysis.

Main Results:

  • RADIANCE achieved state-of-the-art performance, with accuracies of 89.36% (DAIC-WOZ), 80.36% (E-DAIC), and 94.44% (CMDC).
  • Mean Absolute Error (MAE) scores of 3.27 (DAIC-WOZ) and 5.04 (E-DAIC) demonstrate predictive accuracy.
  • The model provides interpretable depression symptoms and reliability scores for clinical utility.

Conclusions:

  • RADIANCE offers a transparent and accurate approach to speech-based depression detection.
  • The method effectively handles data challenges like class imbalance and misclassification.
  • This interpretable AI model holds significant potential for augmenting mental health diagnostics.