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Machine learning-based bladder effusion estimation model construction on intravesical pressure data
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AI
DOI：10.1016/j.bspc.2023.105207.png)
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Abstract
En 中文
Background: The volume of the bladder effusion is a crucial metric for monitoring patients with intermittent catheterization in, and it serves as a crucial benchmark for bladder rehabilitation training. Clinically, bladder effusion is mainly estimated by ultrasound, which is not convenient for real-time monitoring. It is essential to explore a real-time method for obtaining the intravesical effusion volume to maintain bladder function and rehabilitative training for patients.Purpose: Previous clinical trials have demonstrated a close correlation between intravesical pressure and bladder effusion in patients. Currently, there are devices available in clinical practice that can monitor intravesical pressure in real-time. Hence, the aim of this study is to analyze the dynamic relationship between real-time intravesical pressure and bladder effusion and develop a predictive model for bladder effusion.Method: To validate our hypothesis, we compiled a dataset comprising intravesical pressure curves and corre-sponding effusion information from patients. Relevant features were extracted from the dataset, and an effusion prediction model was constructed using multiple regression algorithms. The performance of these algorithms was evaluated using the dataset. Finally, we designed a clinical experiment and verified the practical error of the above model.Results: We conducted sufficient comparative experiments on two datasets. The gradient boosting regression (GBR) model performs the best on the prediction set of the multi-center merged dataset. It achieved an explained variance score index of 0.84 and a mean absolute error of 23.28 ml. Additionally, actual validation experiments were conducted on 18 patients using the GBR method, confirming the feasibility of this approach in clinical practice.Conclusion: This study verified the validity of our concept and pioneered a method for effusion prediction using intravesical pressure data with promising results. This study is expected to be of great assistance in maintaining bladder function and rehabilitative training for patients.
Keywords:
Intravesical pressure
Bladder effusion
Machine learning
Regression prediction model
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