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Background <p>Hemodialysis, a renal replacement treatment for end-stage renal failure, relies heavily on the proper functioning of the dialysis machine. Timely detection and handling of dialysis machine alarms are important to ensure the safety of dialysis treatment.</p> Method <p>This study proposes a method for recognizing dialysis machine alarms using a convolutional neural network (CNN). A dataset of dialysis machine alarm light images was created through a multicenter collaboration, which was used to train the YOLOv5 model.</p> Results <p>The study shows that the average recognition precision, recall, and mAP@0.5 for each warning light category reached 0.892, 0.813, and 0.833, respectively. A well-trained model can quickly and accurately recognize a variety of dialysis machine alarm types.</p> Conclusions <p>It is feasible to use convolutional neural networks to recognize dialysis machine alarms, and they can be widely used to improve dialysis safety and management.</p>

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Dialysis machine alarm recognition based on convolutional neural network

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月11日

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  • Huile Xie,
  • Xiongjie Deng,
  • Bin Dong,
  • Liting Chen,
  • Mingyang Song,
  • Zidong Ying,
  • Zhaoxin Fan,
  • Xukai Wang,
  • Liang Peng

摘要

Background

Hemodialysis, a renal replacement treatment for end-stage renal failure, relies heavily on the proper functioning of the dialysis machine. Timely detection and handling of dialysis machine alarms are important to ensure the safety of dialysis treatment.

Method

This study proposes a method for recognizing dialysis machine alarms using a convolutional neural network (CNN). A dataset of dialysis machine alarm light images was created through a multicenter collaboration, which was used to train the YOLOv5 model.

Results

The study shows that the average recognition precision, recall, and mAP@0.5 for each warning light category reached 0.892, 0.813, and 0.833, respectively. A well-trained model can quickly and accurately recognize a variety of dialysis machine alarm types.

Conclusions

It is feasible to use convolutional neural networks to recognize dialysis machine alarms, and they can be widely used to improve dialysis safety and management.