Bladder compliance in urodynamics, Voiding Dysfunction.
| Article DOI | https://doi.org/10.1080/10255842.2023.2301414 |
| Objective | To develop an efficient bladder compliance screen approach before UDS |
| AI Approach | CNN |
| Data Source(s) | Single institutional series (805 urodynamic series) |
| Model Input | 15 features from urodynamic study (time-based and frequency-based), after principal component analysis |
| Model Outcome | Detrusor overactivity (Time-based and frequency-based) |
| Model Metrics | Time-based detrusor overactivity: AUC 0.92 Frequency-based detrusor overactivity: AUC 0.91 |
| Model Usability | Influence of important predictors provided. |
AI = Artificial intelligence, AUC = Area under the receiver operator characertistic
AIPEDURO 
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