To explore noninvasive diagnostic approaches for glucolipid metabolic disorders using digitalized TCM visual diagnosis technology, specifically focusing on tongue and facial image analysis.
Approach:
Key Findings:
The study indicates the potential of TCM visual diagnostic methods for noninvasive diagnosis of glucolipid metabolic disorders, utilizing specific machine learning techniques.
Machine learning and image processing techniques enhance the accuracy of disease diagnosis based on tongue and facial images.
Interpretation:
The integration of TCM diagnostic methods with modern technology may provide a promising avenue for early detection and management of glucolipid metabolic disorders.
Limitations:
The study is limited to a single center, which may affect the generalizability of the findings, particularly in diverse populations.
Retrospective design may introduce biases in data collection and analysis, potentially impacting the reliability of the results.
Conclusion:
The findings support the use of tongue and facial image analysis as a noninvasive diagnostic tool for glucolipid metabolic disorders, warranting further research and validation to enhance clinical practice.
