Construction of an artificial intelligence-assisted system for auxiliary detection of auricular point features based on the YOLO neural network.
10.13703/j.0255-2930.20240611-0001
- Author:
Ganhong WANG
1
;
Zihao ZHANG
2
;
Kaijian XIA
3
;
Yanting ZHOU
1
;
Meijuan XI
1
;
Jian CHEN
4
Author Information
1. Department of Gastroenterology, Changshu Hospital of TCM, Changshu 215500, Jiangsu Province, China.
2. Shanghai Haoxiong Educational Technology Co., Ltd.
3. Changshu Key Laboratory of Medical Artificial Intelligence and Big Data.
4. Department of Gastroenterology, Changshu First People's Hospital, Changshu 215500, Jiangsu Province.
- Publication Type:Journal Article
- Keywords:
YOLO neural network;
artificial intelligence (AI);
auricular point;
deep learning;
key-point detection
- MeSH:
Humans;
Neural Networks, Computer;
Artificial Intelligence;
Acupuncture Points
- From:
Chinese Acupuncture & Moxibustion
2025;45(4):413-420
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE:To develop an artificial intelligence-assisted system for the automatic detection of the features of common 21 auricular points based on the YOLOv8 neural network.
METHODS:A total of 660 human auricular images from three research centers were collected from June 2019 to February 2024. The rectangle boxes and features of images were annotated using the LabelMe5.3.1 tool and converted them into a format compatible with the YOLO model. Using these data, transfer learning and fine-tuning training were conducted on different scales of pretrained YOLO neural network models. The model's performance was evaluated on validation and test sets, including the mean average precision (mAP) at various thresholds, recall rate (recall), frames per second (FPS) and confusion matrices. Finally, the model was deployed on a local computer, and the real-time detection of human auricular images was conducted using a camera.
RESULTS:Five different versions of the YOLOv8 key-point detection model were developed, including YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, and YOLOv8x. On the validation set, YOLOv8n showed the best performance in terms of speed (225.736 frames per second) and precision (0.998). On the external test set, YOLOv8n achieved the accuracy of 0.991, the sensitivity of 1.0, and the F1 score of 0.995. The localization performance of auricular point features showed the average accuracy of 0.990, the precision of 0.995, and the recall of 0.997 under 50% intersection ration (mAP50).
CONCLUSION:The key-point detection model of 21 common auricular points based on YOLOv8n exhibits the excellent predictive performance, which is capable of rapidly and automatically locating and classifying auricular points.
