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

Abstract

Eucommiae Cortex (ECO) is a traditional medicinal and edible plant endemic to China, highly prized for its numerous health benefits. It typically undergoes special processing before application. The efficacy of ECO is influenced by processing techniques, necessitating the assurance of stability and consistency in its effects. However, existing methods for identifying ECO are cumbersome, thus, there is an urgent need to develop an accurate, rapid, and non-invasive assessment method. Deep learning techniques employing ResNet and Vision Transformer (ViT) models were employed to classify ECO images at various processing levels. Concurrently, the anti-fatigue properties of ECO were assessed through swimming time, pole climbing experiments, and biochemical analyses including SDH, LDH, ATP content, Na⁺-K⁺-ATPase, and Ca²⁺-Mg²⁺-ATPase indices. We demonstrated the efficacy of using image analysis to automatically classify ECO with a high degree of accuracy. The results indicated that the Vision Transformer model performed exceptionally well, achieving an accuracy rate exceeding 95 % in grading ECO images. Additionally, our study revealed that mice treated with moderately processed ECO displayed enhanced fatigue mitigation compared to other processing levels, as evidenced by multiple assessments.