A novel predictive method for URS and laser lithotripsy using machine learning and explainable AI: results from the FLEXOR international database
Bibliographic record
Abstract
PURPOSE: We developed Machine learning (ML) algorithms to predict ureteroscopy (URS) outcomes, offering insights into diagnosis and treatment planning, personalised care and improved clinical decision-making. METHODS: FLEXOR 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. RESULTS: Extra 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). CONCLUSION: ML 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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".