Development of an abdominal acupoint localization system based on AI deep learning.
10.13703/j.0255-2930.20240207-0003
- Author:
Mo ZHANG
1
;
Yuming LI
2
;
Zongming SHI
1
Author Information
1. Department of TCM, Integration of Traditional Chinese and Western Medicine, First Hospital of Peking University, Beijing 100034, China.
2. Department of Electronic Engineering, City University of Hong Kong, Hong Kong
- Publication Type:Journal Article
- Keywords:
acupoint localization;
acupuncture;
convolutional neural network (CNNs);
machine learning
- MeSH:
Acupuncture Points;
Humans;
Deep Learning;
Abdomen/diagnostic imaging*;
Neural Networks, Computer;
Acupuncture Therapy;
Image Processing, Computer-Assisted
- From:
Chinese Acupuncture & Moxibustion
2025;45(3):391-396
- CountryChina
- Language:Chinese
-
Abstract:
This study aims to develop an abdominal acupoint localization system based on computer vision and convolutional neural networks (CNNs). To address the challenge of abdominal acupoint localization, a multi-task CNNs architecture was constructed and trained to locate the Shenque (CV8) and human body boundaries. Based on the identified Shenque (CV8), the system further deduces key characteristics of four acupoints: Shangwan (CV13), Qugu (CV2), and bilateral Daheng (SP15). An affine transformation matrix is applied to accurately map image coordinates to an acupoint template space, achieving precise localization of abdominal acupoints. Testing has verified that this system can accurately identify and locate abdominal acupoints in images. The development of this localization system provides technical support for TCM remote education, diagnostic assistance, and advanced TCM equipment, such as intelligent acupuncture robots, facilitating the standardization and intelligent advancement of acupuncture.
