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Tongue feature dataset construction and real-time detection.
Chang, Wen-Hsien; Chen, Chih-Chieh; Wu, Han-Kuei; Hsu, Po-Chi; Lo, Lun-Chien; Chu, Hsueh-Ting; Chang, Hen-Hong.
Afiliación
  • Chang WH; Graduate Institute of Chinese Medicine, School of Chinese Medicine, College of Chinese Medicine, China Medical University, Taichung, Taiwan, Republic of China.
  • Chen CC; Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan, Republic of China.
  • Wu HK; School of Post-Baccalaureate Chinese Medicine, College of Chinese Medicine, China Medical University, Taichung, Taiwan, Republic of China.
  • Hsu PC; Department of Traditional Chinese Medicine, Kuang Tien General Hospital, Taichung, Taiwan, Republic of China.
  • Lo LC; Department of Traditional Chinese Medicine, Kuang Tien General Hospital, Taichung, Taiwan, Republic of China.
  • Chu HT; School of Chinese Medicine, College of Chinese Medicine, China Medical University, Taichung, Taiwan, Republic of China.
  • Chang HH; School of Chinese Medicine, College of Chinese Medicine, China Medical University, Taichung, Taiwan, Republic of China.
PLoS One ; 19(3): e0296070, 2024.
Article en En | MEDLINE | ID: mdl-38452007
ABSTRACT

BACKGROUND:

Tongue diagnosis in traditional Chinese medicine (TCM) provides clinically important, objective evidence from direct observation of specific features that assist with diagnosis. However, the current interpretation of tongue features requires a significant amount of manpower and time. TCM physicians may have different interpretations of features displayed by the same tongue. An automated interpretation system that interprets tongue features would expedite the interpretation process and yield more consistent results. MATERIALS AND

METHODS:

This study applied deep learning visualization to tongue diagnosis. After collecting tongue images and corresponding interpretation reports by TCM physicians in a single teaching hospital, various tongue features such as fissures, tooth marks, and different types of coatings were annotated manually with rectangles. These annotated data and images were used to train a deep learning object detection model. Upon completion of training, the position of each tongue feature was dynamically marked.

RESULTS:

A large high-quality manually annotated tongue feature dataset was constructed and analyzed. A detection model was trained with average precision (AP) 47.67%, 58.94%, 71.25% and 59.78% for fissures, tooth marks, thick and yellow coatings, respectively. At over 40 frames per second on a NVIDIA GeForce GTX 1060, the model was capable of detecting tongue features from any viewpoint in real time. CONCLUSIONS/

SIGNIFICANCE:

This study constructed a tongue feature dataset and trained a deep learning object detection model to locate tongue features in real time. The model provided interpretability and intuitiveness that are often lacking in general neural network models and implies good feasibility for clinical application.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Lengua / Redes Neurales de la Computación Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Lengua / Redes Neurales de la Computación Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos