Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Faculty of Health and Social Sciences | en_US |
| dc.contributor.advisor | Qin, Harry (SN) | en_US |
| dc.creator | Ho, Siu Cheong | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/14195 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | Tongue diagnosis of TCM via advanced computer vision techniques enhancing remote TCM tongue diagnosis : a mixed-methods study on ai-assisted tongue feature recognition and regional analysis | en_US |
| dcterms.abstract | Background: The integration of Traditional Chinese Medicine (TCM) into telemedicine has encountered challenges related to the standardization of image quality and diagnostic accuracy in remote tongue diagnosis. This study seeks to address these issues by developing and validating AI systems designed to enhance the reliability and effectiveness of remote TCM consultations. | en_US |
| dcterms.abstract | Methods: Employing a sequential mixed-methods design, the research commenced with focus group interviews involving experienced TCM practitioners to gather insights on the practical challenges of remote diagnosis. These findings informed the creation of two specialized AI systems: SignNet for holistic tongue feature recognition, trained on 5,109 mobile-captured tongue images, and OrganNet for regional analysis, utilizing 4,645 images with organ-specific annotations. Both systems incorporate advanced Convolutional Neural Networks (CNN) architectures tailored to align with traditional TCM diagnostic principles. | en_US |
| dcterms.abstract | Results: The qualitative phase identified significant challenges in remote diagnosis, including image variability, technical issues, and concerns over diagnostic accuracy. SignNet outperformed existing models by approximately 4% in F1-score, while OrganNet achieved an average accuracy of 81.34% in assessing organ conditions, closely mirroring the performance of human experts. Notably, OrganNet excelled in assessing lung (72.29% accuracy) and kidney (85.83% F1-score) conditions, demonstrating the potential of AI to standardize and enhance remote TCM diagnostics. | en_US |
| dcterms.abstract | Conclusions: This research contributes significantly to the field by introducing comprehensive datasets for real-world mobile-captured tongue images, proposing innovative CNN-based AI systems that align with TCM principles, and establishing a framework for AI integration into TCM practice. While the AI systems showed promise in enhancing diagnostic consistency and accuracy, challenges remain, particularly in detecting subtle tongue features under variable lighting conditions. Future research should focus on improving feature detection, integrating with other TCM diagnostic methods, and expanding the application to broader TCM diagnostic areas. This study paves the way for more reliable and accessible remote TCM consultations, potentially revolutionizing telemedicine in traditional medicine. | en_US |
| dcterms.extent | xxi, 193 pages : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2025 | en_US |
| dcterms.educationalLevel | DHSc | en_US |
| dcterms.educationalLevel | All Doctorate | en_US |
| dcterms.accessRights | restricted access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 8647.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 2.53 MB | Adobe PDF | View/Open |
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