Table 2.
Summary of studies regarding AI in the prediction of management outcomes.
| Study | Objective | Study design | AI-based outcome | Comparator arm outcome |
|---|---|---|---|---|
| Cummings et al. [30] | Prediction of SSP | Case-control | Accuracy of 76% | No comparator |
| Dal Moro et al. [31] | Prediction of SSP | Case-control | 84.5% sensitivity and 86.9% specificity | Other algorithms with lower performance |
| Solakhan et al. [32] | Prediction of SSP | Case-control | Accuracy of 92.8% | Other algorithms with lower performance |
| Park et al. [33] | Prediction of SSP | Case-control | AUCs of 0.859 (stones of <5 mm) and 0.881 (stones of 5–10 mm) | AUC of 0.847 (stones of <5 mm) and 0.817 (stones of 5 mm–10 mm) |
| Poulakis et al. [34] | Prediction of lower pole clearance after ESWL | Case-control | Accuracy of 92% | No comparator |
| Gomha et al. [35] | Prediction of clearance after ESWL for ureteral stones | Case-control | Accuracy of 77.7% | Accuracy of 93.2% |
| Moorthy and Krishnan [36] | Prediction of renal stone fragmentation after ESWL | Case-control | Accuracy of 90% | No comparator |
| Choo et al. [37] | Prediction of clearance after ESWL for ureteral stones | Case-control | Accuracy of 92.29% | No comparator |
| Seckiner et al. [38] | Prediction of clearance after ESWL for renal stones | Case-control | Accuracy of 88.70% | No comparator |
| Mannil et al. [39] | Prediction of renal stones fragmentation after ESWL | Case-control | AUC of 0.85 | Other algorithms with lower performance |
| Yang et al. [40] | Prediction of clearance after ESWL for renal or upper ureter stones | Case-control | AUC of 0.85 for stone-free status in an interval of 4 weeks; AUC of 0.78 for stone-free status after single session ESWL | Other algorithms with similar performance |
| Tsitsiflis et al. [41] | Prediction of complications after ESWL for renal or ureteral stones | Case-control | Accuracy of 81.43% | No comparator |
| Handa et al. [42] | Quantification of ESWL-induced renal injury by MRI | Experimental | Strong correlation between model prediction and morphology (r=0.9691) | No comparator |
| Aminsharifi et al. [43] | Prediction of multiple outcomes after PCNL | Case-control | Accuracy of 91.8%, 83% regarding stone clearance and need for blood transfusion; AUC of 0.915 for stone clearance | AUCs of 0.615 and 0.621 for stone clearance according to GSS and CROES nomograms |
| Shabaniyan et al. [44] | Prediction of multiple outcomes after PCNL | Case-control | Accuracy of 94.8% in prediction of the procedures‘ outcome, 85.2% accuracy in predicting the need for stent placement and 95% in predicting blood transfusion | Multiple decision support systems achieving higher performances in different parameters |
| Aminsharifi et al. [45] | Prediction of multiple outcomes after PCNL | Case-control | Accuracy of 82.8%, 92.5%–98.2%, 81.1%, and 85.8% for stone clearance, need for a second procedure, stent insertion by urine extravasation, and blood transfusion | No comparator |
| Geraghty et al. [46] | Prediction of multiple outcomes after PCNL | Case-control | Multiple classification models tested, highest accuracy of 99% and AUCs of 0.99–1.00 achieved for need for transfusion and infectious complications | No comparator |
| Zhao et al. [47] | Prediction of stone clearance after PCNL | Case-control | AUC of 0.879 | AUC of 0.800 for GSS; AUC of 0.844 for S.T.O.N.E. score |
| Chen et al. [48] | Prediction of sepsis after fURS or PCNL for proximal ureteral stones | Case-control | AUC of 0.874 for DNN model | AUC of 0.783 for LASSO model |
AI, artificial intelligence; AUC, area under the curve; CROES, Clinical Research Office of the Endourological Society; DNN, deep neural network; ESWL, extracorporeal shockwave lithotripsy; fURS, flexible uretero-renoscopy; GSS, Guy's stone score; LASSO, least absolute shrinkage and selection operator; PCNL, percutaneous nephrolithotomy; SSP, spontaneous stone passage; S.T.O.N.E., stone size (S), tract length (T), obstruction (O), number of involved calices (N), and essence or stone density (E); MRI, magnetic resonance imaging.