KMID : 1240020230270040280
|
|
International Neurourology Journal 2023 Volume.27 No. 4 p.280 ~ p.286
|
|
Artificial Intelligence-Based Patient Monitoring System for Medical Support
|
|
Kim Eui-Sun
Eun Sung-Jong Kim Khae-Hawn
|
|
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.
|
|
KEYWORD
|
|
Urination recognition, Deep learning, Diagnosis support system, Patient monitoring system, Neurogenic bladder
|
|
FullTexts / Linksout information
|
|
|
|
Listed journal information
|
|
|
|