A novel deep learning based cloud service system for automated acupuncture needle counting: a strategy to improve acupuncture safety

Objective: The unintentional retention of needles in patients can lead to severe consequences. To enhance acupuncture safety, the study aimed to develop a deep learning-based cloud system for automated process of counting acupuncture needles. Methods: This project adopted transfer learning from a pre-trained Oriented Region-based Convolutional Neural Network (Oriented R-CNN) model to develop a detection algorithm that can automatically count the number of acupuncture needles in a camera picture. A training set with 590 pictures and a validation set with 1 025 pictures were accumulated for fine-tuning. Then, we deployed the MMRotate toolbox in a Google Colab environment with a NVIDIA Tesla T4 Graphics processing unit (GPU) to carry out the training task. Furthermore, we integrated the model with a newly-developed Telegram bot interface to determine the accuracy, precision, and recall of the needling counting system. The end-to-end inference time was also recorded to determine the speed of our cloud service system. Results: In a 20-needle scenario, our Oriented R-CNN detection model has achieved an accuracy of 96.49%, precision of 99.98%, and recall of 99.84%, with an average end-to-end inference time of 1.535 s Conclusion: The speed, accuracy, and reliability advancements of this cloud service system innovation have demonstrated its potential of using object detection technique to improve acupuncture practice based on deep learning.