Reliability and Reproducibility of Pediatric Urodynamic Studies

CNN-based diagnosis model of children's bladder compliance using a single intravesical pressure signal

Bladder compliance assessment is crucial for diagnosing bladder functional disorders, with urodynamic study (UDS) being the principal evaluation method. However, the application of UDS is intricate and time-consuming in children. So it'S necessary to develop an efficient bladder compliance screen approach before UDS. In this study, We constructed a dataset based on UDS and designed a 1D-CNN model to optimize and train the network. Then applied the trained model to a dataset obtained solely through a proposed perfusion experiment. Our model outperformed other algorithms. The results demonstrate the potential of our model to alert abnormal bladder compliance accurately and efficiently.

Machine Learning for Urodynamic Detection of Detrusor Overactivity

OBJECTIVE

To develop a machine learning algorithm that identifies detrusor overactivity (DO) in Urodynamic Studies (UDS) in the spina bifida population. UDS plays a key role in assessment of neurogenic bladder in patients with spina bifida. Due to significant variability in individual interpretations of UDS data, there is a need to standardize UDS interpretation.

MATERIALS AND METHODS

Patients who underwent UDS at a single pediatric urology clinic between May 2012 and September 2020 were included. UDS files were analyzed in both time and frequency domains, varying inclusion of vesical, abdominal, and detrusor pressure channels. A machine learning pipeline was constructed using data windowing, dimensionality reduction, and support vector machines. Models were designed to detect clinician identified detrusor overactivity.

RESULTS

Data were extracted from 805 UDS testing files from 546 unique patients. The generated models achieved good performance metrics in detecting DO agreement with the clinician, in both time- and frequency-based approaches. Incorporation of multiple channels and data windowing improved performance. The time-based model with all 3 channels had the highest area under the curve (AUC) (91.9 ± 1.3%; sensitivity: 84.2 ± 3.8%; specificity: 86.4 ± 1.3%). The 3-channel frequency-based model had the highest specificity (AUC: 90.5 ± 1.9%; sensitivity: 68.3 ± 5.3%; specificity: 92.9 ± 1.1%).

CONCLUSION

We developed a promising proof-of-concept machine learning pipeline that identifies DO in UDS. Machine-learning-based predictive modeling algorithms may be employed to standardize UDS interpretation and could potentially augment shared decision-making and improve patient care.