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ORIGINAL RESEARCH article

Front. Psychiatry
Sec. Mood Disorders
Volume 15 - 2024 | doi: 10.3389/fpsyt.2024.1361177

Evaluating Deep Learning Techniques for Identifying Tongue Features in Subthreshold Depression: A Prospective Observational Study

Provisionally accepted
Bo Han Bo Han 1Yue Chang Yue Chang 2Rui-rui Tan Rui-rui Tan 3Chao Han Chao Han 4*
  • 1 Daqing Longnan Hospital, Daqing, China
  • 2 Baoan Central Hospital, Fifth Affiliated Hospital of Shenzhen University, Shenzhen, China
  • 3 Changchun University of Chinese Medicine, Changchun, Jilin Province, China
  • 4 Shenzhen Bao'an Authentic TCM Therapy Hospital, Shenzhen, China

The final, formatted version of the article will be published soon.

    Objective: This study aims to evaluate the potential of using tongue image features as non-invasive biomarkers for diagnosing subthreshold depression and to assess the correlation between these features and acupuncture treatment outcomes using advanced deep learning models.Methods: We employed five advanced deep learning models-DenseNet169, MobileNetV3Small, SEResNet101, SqueezeNet, and VGG19_bn-to analyze tongue image features in individuals with subthreshold depression. These models were assessed based on accuracy, precision, recall, and F1 score. Additionally, we investigated the relationship between the best-performing model's predictions and the success of acupuncture treatment using Pearson's correlation coefficient.Results: Among the models, SEResNet101 emerged as the most effective, achieving an impressive 98.5% accuracy and an F1 score of 0.97. A significant positive correlation was found between its predictions and the alleviation of depressive symptoms following acupuncture (Pearson's correlation coefficient = 0.72, p<0.001).The findings suggest that the SEResNet101 model is highly accurate and reliable for identifying tongue image features in subthreshold depression. It also appears promising for assessing the impact of acupuncture treatment. This study contributes novel insights and approaches to the auxiliary diagnosis and treatment evaluation of subthreshold depression.

    Keywords: Subthreshold depression, Tongue image features, deep learning, SEResNet101, Acupuncture treatment, Correlation analysis

    Received: 25 Dec 2023; Accepted: 15 Jul 2024.

    Copyright: © 2024 Han, Chang, Tan and Han. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Chao Han, Shenzhen Bao'an Authentic TCM Therapy Hospital, Shenzhen, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.