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 conse-quences.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 train-ing 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 ac-curacy,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 accu-racy of 96.49%,precision of 99.98%,and recall of 99.84%,with an average end-to-end infer-ence 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.
国家科技期刊平台
NSTL
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