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Fatigue

Fatigue

Fatigue occurs when materials rupture under repeated or fluctuating loads, even at stress levels far below their static breaking strength. It typically results in brittle failure, even for ductile materials. It is a critical consideration in designing machines and structural components subjected to repetitive or varying loads. The nature of these loadings can range from fluctuating loads like unbalanced pump impellers causing vibrations to repeatedly bending a thin steel rod wire back and forth...

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Leveraging AI technology for distinguishing Eucommiae Cortex processing levels and evaluating anti-fatigue potential.

Yijing Pan¹, Shunshun Wang¹, Kehong Ming¹

¹Hubei Provincial Engineering Technology Research Center for Chinese Medicine Processing, School of Pharmacy, Hubei University of Chinese Medicine, Wuhan, 430065, China; Hubei Shizhen Laboratory, Wuhan, 430065, China.

Computers in Biology and Medicine

|November 17, 2024

Summary

This study developed a deep learning image analysis method to accurately classify processed Eucommiae Cortex (ECO). Moderately processed ECO demonstrated the best anti-fatigue effects in mice.

Keywords:

Anti-fatigue Automatic classification Eucommiae cortex ResNet Vision transformer

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Area of Science:

Traditional Chinese Medicine
Pharmacognosy
Computational Biology

Background:

Eucommiae Cortex (ECO) is a valued medicinal plant requiring specific processing for efficacy.
Processing methods significantly impact ECO's therapeutic effects, necessitating quality control.
Current identification methods for ECO are inefficient and invasive.

Purpose of the Study:

To develop an accurate, rapid, and non-invasive method for classifying processed ECO using image analysis.
To evaluate the anti-fatigue properties of ECO processed at different levels.
To correlate image-based classification with functional anti-fatigue efficacy.

Main Methods:

Deep learning models, including ResNet and Vision Transformer (ViT), were trained on images of ECO at various processing stages.
Anti-fatigue efficacy was assessed in mice via swimming endurance, pole climbing, and biochemical markers (SDH, LDH, ATP, Na+-K+-ATPase, Ca2+-Mg2+-ATPase).
Image classification accuracy and anti-fatigue performance across different processing levels were analyzed.

Main Results:

The Vision Transformer model achieved over 95% accuracy in classifying ECO images based on processing levels.
Image analysis effectively provided an automated and accurate method for ECO quality assessment.
Mice treated with moderately processed ECO showed superior anti-fatigue benefits compared to other groups.

Conclusions:

Deep learning-based image analysis offers a promising, non-invasive tool for quality control of processed Eucommiae Cortex.
Processing level is critical for optimizing the anti-fatigue properties of ECO.
Further research can leverage AI for standardization of traditional herbal medicines.