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Related Concept Videos

Long-term Depression01:05

Long-term Depression

Long-term depression, or LTD, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTD is the process of synaptic weakening that occurs over time between pre and postsynaptic neuronal connections. The synaptic weakening of LTD works in opposition to synaptic strengthening by long-term potentiation (LTP) and together are the main mechanisms that underlie learning and memory.
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Ultrasound Images of the Tongue: A Tutorial for Assessment and Remediation of Speech Sound Errors
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Evaluating deep learning techniques for identifying tongue features in subthreshold depression: a prospective

Bo Han1, Yue Chang2, Rui-Rui Tan3

  • 1Department of Rehabilitation, Daqing Longnan Hospital, Daqing, China.

Frontiers in Psychiatry
|August 23, 2024
PubMed
Summary
This summary is machine-generated.

Tongue image analysis using the SEResNet101 deep learning model shows high accuracy in diagnosing subthreshold depression. This method also shows promise for evaluating acupuncture treatment effectiveness.

Keywords:
SEResNet101acupuncture treatmentcorrelation analysisdeep learningsubthreshold depressiontongue image features

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Area of Science:

  • Integrative Medicine
  • Computational Psychiatry
  • Medical Imaging Analysis

Background:

  • Subthreshold depression requires accessible diagnostic tools.
  • Non-invasive biomarkers are crucial for early detection.
  • Tongue image analysis offers a novel diagnostic avenue.

Purpose of the Study:

  • To evaluate tongue image features as biomarkers for subthreshold depression.
  • To assess the correlation between tongue features and acupuncture outcomes.
  • To apply advanced deep learning models for diagnosis and treatment evaluation.

Main Methods:

  • Utilized five deep learning models (DenseNet169, MobileNetV3Small, SEResNet101, SqueezeNet, VGG19_bn) for tongue image analysis.
  • Assessed model performance using accuracy, precision, recall, and F1 score.
  • Employed Pearson's correlation to link model predictions with acupuncture treatment success.

Main Results:

  • The SEResNet101 model demonstrated superior performance with 98.5% accuracy and 0.97 F1 score.
  • A significant positive correlation (r=0.72, p<0.001) was observed between SEResNet101 predictions and symptom improvement after acupuncture.
  • Tongue image features show potential as reliable indicators.

Conclusions:

  • The SEResNet101 model is accurate and reliable for diagnosing subthreshold depression via tongue images.
  • This approach shows promise for monitoring acupuncture treatment efficacy.
  • The study offers innovative methods for auxiliary diagnosis and treatment assessment in subthreshold depression.