Machine learning (ML) and artificial intelligence (AI) have demonstrated powerful functionality in the healthcare setting thus far. We aimed to construct an AI model to predict postoperative incontinence after enucleation surgery for benign prostatic hyperplasia (BPH).
Data were taken from two BPH registries and split into training and validation datasets. The following characteristics were used as predictors of incontinence: age, prostate volume, preoperative IPSS, QoL score, Qmax and post-void residual; presence of preoperative indwelling catheter, early apical release (EAR), enucleation type (2-lobe, 3-lobe, or en-bloc), and laser energy type. Six types of ML models were constructed using the training dataset and applied to the validation dataset to assess their accuracy.
3828 patients from both databases were analyzed. Median age was 68, median prostate volume was 85.5 cc. 5.4% had a preoperative indwelling catheter. The commonest enucleation type was 2-lobe, the commonest energy type was Thulium fiber laser, and EAR was performed in 34.0%. Of the six ML models tested, extreme gradient boosting with manual fine tuning was the best-performing with an accuracy of 86.2%, sensitivity of 96.8%, specificity of 23.7%, PPV of 88.2%, and NPV of 55.9%.
We hereby present an ML model for incontinence prediction post-surgery for BPH. Its main strengths are high sensitivity and PPV, meaning that if a patient is predicted to be incontinent using this model, this is likely to reflect the eventual outcome. This allows clinicians to pay closer attention on follow-up to detect and manage postoperative incontinence expediently.
Prostate cancer and prostatic diseases. 2025 Aug 19 [Epub ahead of print]
Khi Yung Fong, Vineet Gauhar, Thomas R W Herrmann, Carlotta Nedbal, Dmitry Enikeev, Jeremy Yuen-Chun Teoh, Sarvajit Biligere, Steffi Kar Kei Yuen, Daniele Castellani, Bhaskar Kumar Somani, Patrick Juliebø-Jones, Valerie Huei Li Gan, Edwin Jonathan Aslim, Ee Jean Lim
Department of Urology, Singapore General Hospital, Singapore, Singapore. ., Department of Urology, Ng Teng Fong General Hospital, Singapore, Singapore., Department of Urology, Spital Thurgau AG, Frauenfeld, Switzerland., Urology Unit, Azienda Ospedaliero-Universitaria delle Marche, Ancona, Italy., Institute for Urology and Reproductive Health, Sechenov University, Moscow, Russian Federation., SH Ho Urology Centre, Department of Surgery, The Chinese University of Hong Kong, Hong Kong, China., Endourology section of the European Association of Urology, Arnhem, Netherlands., Department of Urology, Haukeland University Hospital, Bergen, Norway., Department of Urology, Singapore General Hospital, Singapore, Singapore.
PubMed http://www.ncbi.nlm.nih.gov/pubmed/40830571