Dual-diagnostic Method for Depression Patients Based on BERT Model and ViT Model for Audio and Text Analysis

In the face of escalating global mental health challenges, the rapid and accurate diagnosis of depression has become more crucial than ever. Depression, a pervasive mental health disorder, often goes undiagnosed due to its complex symptomatology and the limitations inherent in traditional diagnostic methods. Current approaches, relying heavily on patient self-reporting and clinical observation, fall short in capturing the multifaceted nature of depression, leading to potential delays in treatment and, consequently, exacerbated health outcomes. Addressing these shortcomings, this paper introduces a novel dual-diagnostic approach leveraging the advanced capabilities of AI, specifically through the integration of BERT and Vision Transformer (ViT) models. Our methodology utilizes BERT for the analysis of conversational text data and ViT for the examination of audio spectrograms from patients with depression, offering a comprehensive and nuanced understanding of depressive states. This dual-modality approach not only addresses the limitations of existing single-mode diagnostic practices but also significantly enhances the accuracy and efficiency of depression diagnosis. By harnessing the strengths of both textual and auditory data analysis, our proposed method stands as a testament to the potential of AI in revolutionizing mental health diagnostics, paving the way for timely interventions and personalized care strategies that are critically needed in the medical and clinical domain.