Harnessing Artificial Intelligence to Predict Spontaneous Stone Passage: Development and Testing of a Machine Learning-Based Calculator

Objective: We sought to use artificial intelligence (AI) to develop and test calculators to predict spontaneous stone passage (SSP) using radiographical and clinical data. Methods: Consecutive patients with solitary ureteral stones ≤10 mm on CT were prospectively enrolled and managed according to American Urological Association guidelines. The first 70% of patients were placed in the “training group” and used to develop the calculators. The latter 30% were enrolled in the “testing group” to externally validate the calculators. Exclusion criteria included contraindication to trial of SSP, ureteral stent, and anatomical anomaly. Demographic, clinical, and radiographical data were obtained and fed into machine learning (ML) platforms. SSP was defined as passage of stone without intervention. Calculators were derived from data using multivariate logistic regression. Discrimination, calibration, and clinical utility/net benefit of the developed models were assessed in the validation cohort. Receiver operating characteristic curves were constructed to measure their discriminative ability. Results: Fifty-one percent of 131 “training” patients spontaneously passed their stones. Passed stones were significantly closer to the bladder (8.6 vs 11.8 cm, p = 0.01) and smaller in length, width, and height. Two ML calculators were developed, one supervised machine learning (SML) and the other unsupervised machine learning (USML), and compared to an existing tool Multi-centre Cohort Study Evaluating the role of Inflammatory Markers In Patients Presenting with Acute Ureteric Colic (MIMIC). The SML calculator included maximum stone width (MSW), ureteral diameter above the stone (UDA), and distance from ureterovesical junction to bottom of stone and had an area under the curve (AUC) of 0.737 upon external validation of 58 “test” patients. Parameters selected by USML included MSW, UDA, and use of an anticholinergic, and it had an AUC of 0.706. The MIMIC calculator’s AUC was 0.588 (0.489–0.686). Conclusion: We used AI to develop calculators that outperformed an existing tool and can help providers and patients make a better-informed decision for the treatment of ureteral stones.