A novel predictive method for URS and laser lithotripsy using machine learning and explainable AI: results from the FLEXOR international database.

purposeWe developed Machine learning (ML) algorithms to predict ureteroscopy (URS) outcomes, offering insights into diagnosis and treatment planning, personalised care and improved clinical decision-making.

methodsFLEXOR is a large international multicentric database including 6669 patients treated with URS for urolithiasis from 2015 to 2023. Preoperative and postoperative(PO) correlations were investigated through 15 ML-trained algorithms. Outcomes included stone free status (SFS, at 3-month imaging follow up), intraoperative (PCS bleeding, ureteric/PCS injury, need for postoperative drainage) and PO complications (fever, sepsis, need for reintervention). ML was applied for the prediction, correlation and logistic regression analysis. Explainable AI emphasizes key features and their contributions to the output.

resultsExtra Tree Classifier achieved the best accuracy (81%) in predicting SFS. PCS bleed was negatively linked with 'positive urine culture'(-0.08), 'tamsulosin'(-0.08), 'stone location'(-0.10), 'fibre optic scope'(-0.19), 'Moses Fibre'(-0.09), and 'TFL'(-0.09), and positively with 'elevated creatine'(0.25), 'fever'(0.11), and 'stone diameter'(0.21). 'PCS injury' and 'ureteric injury' both showed moderate correlation with 'elevated creatinine'(0.11), 'fever'(0.10), and 'lower pole stone'(0.09). 'Tamsulosin'(0.23) use, presence of 'multiple'(0.25) or 'lower pole'(0.25) stones, 'reusable scope'(0.17) and 'Moses Fibre'(0.2546) increased the risk for PO stent, while 'digital scope'(-0.13) or 'TFL'(-0.29) reduced it. 'Preoperative fever'(0.10), 'positive urine culture'(0.16), and 'stone diameter'(0.10) may play a role in 'PO fever' and 'sepsis'. SFS was mainly influenced by 'age'(0.12), 'preoperative fever'(0.09), 'multiple stones'(0.15), 'stone diameter'(0.17), 'Moses Fibre"(0.15) and 'TFL'(-0.28).

conclusionML is valuable tool for accurately predicting outcomes by analysing pre-existing datasets. Our model demonstrated strong performance in outcomes and risks prediction, laying the groundwork for development of accessible predictive models.