Artificial Intelligence-Based Patient Monitoring System for Medical Support
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Purpose: In this paper, we present the development of a monitoring system designed to aid in the management and prevention of conditions related to urination. The system features an artificial intelligence (AI)-based recognition technology that automatically records a user’s urination activity. Additionally, we developed a technology that analyzes movements to prevent neurogenic bladder. Methods: Our approach included the creation of AI-based recognition technology that automatically logs users’ urination activities, as well as the development of technology that analyzes movements to prevent neurogenic bladder. Initially, we employed a recurrent neural network model for the urination activity recognition technology. For predicting the risk of neurogenic bladder, we utilized convolutional neural network (CNN)-based AI technology. Results: The performance of the proposed system was evaluated using a study population of 30 patients with urinary tract dysfunction, who collected data over a 60-day period. The results demonstrated an average accuracy of 94.2% in recognizing urinary tract activity, thereby confirming the effectiveness of the recognition technology. Furthermore, the motion analysis technology for preventing neurogenic bladder, which also employed CNN-based AI, showed promising results with an average accuracy of 83%. Conclusions: In this study, we developed a urination disease monitoring system aimed at predicting and managing risks for patients with urination issues. The system is designed to support the entire care cycle of a patient by leveraging AI technology that processes various image and signal data. We anticipate that this system will evolve into digital treatment products, ultimately providing therapeutic benefits to patients.
Abstract
Purpose: In this paper, we present the development of a monitoring system designed to aid in the management and prevention of conditions related to urination. The system features an artificial intelligence (AI)-based recognition technology that automatically records a user’s urination activity. Additionally, we developed a technology that analyzes movements to prevent neurogenic bladder. Methods: Our approach included the creation of AI-based recognition technology that automatically logs users’ urination activities, as well as the development of technology that analyzes movements to prevent neurogenic bladder. Initially, we employed a recurrent neural network model for the urination activity recognition technology. For predicting the risk of neurogenic bladder, we utilized convolutional neural network (CNN)-based AI technology. Results: The performance of the proposed system was evaluated using a study population of 30 patients with urinary tract dysfunction, who collected data over a 60-day period. The results demonstrated an average accuracy of 94.2% in recognizing urinary tract activity, thereby confirming the effectiveness of the recognition technology. Furthermore, the motion analysis technology for preventing neurogenic bladder, which also employed CNN-based AI, showed promising results with an average accuracy of 83%. Conclusions: In this study, we developed a urination disease monitoring system aimed at predicting and managing risks for patients with urination issues. The system is designed to support the entire care cycle of a patient by leveraging AI technology that processes various image and signal data. We anticipate that this system will evolve into digital treatment products, ultimately providing therapeutic benefits to patients.
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