Dialysis machine alarm recognition based on convolutional neural network

Abstract 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 [email protected] 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.

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Publication Details

Journal
Renal Replacement Therapy
Published
2025-06-11
DOI
https://doi.org/10.1186/s41100-025-00632-9
Primary Topic
Non-Invasive Vital Sign Monitoring
Type
article
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article

Dialysis machine alarm recognition based on convolutional neural network

Hualong Xie, Mingyang Song, Peng Liang, Ying Zhu et al.
Renal Replacement Therapy
Non-Invasive Vital Sign Monitoring
article

Dialysis machine alarm recognition based on convolutional neural network

Hualong Xie, Mingyang Song, Peng Liang, Ying Zhu, Xiongjie Deng, Zheng Fan, Liting Chen, Bin Dong, Xukai Wang
article en

Abstract

Abstract 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 [email protected] 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.

Renal Replacement TherapyVol. 11(1)
Central South University (CN), Shanghai Jiao Tong University (CN), Chinese Academy of Medical Sciences & Peking Union Medical College (CN), Peking Union Medical College Hospital (CN), JW Medical Systems (China) (CN), Beijing Hospital (CN), Shanghai Sixth People's Hospital (CN), Second Xiangya Hospital of Central South University (CN), Third Xiangya Hospital (CN), Weihai Science and Technology Bureau (CN), Changsha University of Science and Technology (CN)
Openalex Percentile: Top 88%
Non-Invasive Vital Sign Monitoring
DOIOpen Access PDFOpenAlex
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