Reports from National Tsing Hua University Add New Study Findings to Research in Traditional Chinese Medicine (Pioneering Data Processing for Convolutional Neural Networks to Enhance the Diagnostic Accuracy of Traditional Chinese Medicine Pulse...).
In: Diabetes Week, 2024-06-17, S. 239-239
serialPeriodical
Zugriff:
A recent study conducted by researchers at National Tsing Hua University in Taiwan has explored the use of deep learning algorithms to enhance the accuracy of traditional Chinese medicine (TCM) pulse diagnosis for diabetes. TCM pulse diagnosis has historically relied on subjective interpretation and theoretical analysis, leading to challenges in diagnostic accuracy and consistency. By applying advanced algorithms to analyze TCM pulse waveforms, the researchers found promising results in reducing practitioner-dependent variability and improving the reliability of diagnoses. The study suggests potential avenues for future research, including optimizing preprocessing techniques and expanding the scope to other diseases, with the aim of refining TCM pulse diagnosis and integrating it into modern technology for more effective healthcare approaches. [Extracted from the article]
Copyright of Diabetes Week is the property of NewsRx and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Titel: |
Reports from National Tsing Hua University Add New Study Findings to Research in Traditional Chinese Medicine (Pioneering Data Processing for Convolutional Neural Networks to Enhance the Diagnostic Accuracy of Traditional Chinese Medicine Pulse...).
|
---|---|
Zeitschrift: | Diabetes Week, 2024-06-17, S. 239-239 |
Veröffentlichung: | 2024 |
Medientyp: | serialPeriodical |
ISSN: | 1537-1425 (print) |
Schlagwort: |
|
Sonstiges: |
|