Objective

2

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

2

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

2

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 (mAP

50

).

Conclusion

2

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.