Artificial Intelligence-Based Patient Monitoring System for Medical Support
| Autor: | Eui-Sun Kim, Sung-Jong Eun, Khae-Hawn Kim |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | International Neurourology Journal, Vol 27, Iss 4, Pp 280-286 (2023) |
| Druh dokumentu: | article |
| ISSN: | 2093-4777 2093-6931 |
| DOI: | 10.5213/inj.2346338.169 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
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