Objectives: To perform a systematic review on artificial intelligence(AI) performances to detect urinary stones.
Methods: A PROSPERO-registered(CRD473152) systematic search of Scopus, Web of Science, Embase and PubMed databases was performed to identify original research articles pertaining to AI stone detection or measurement, using search terms("automatic" OR "machine learning" OR "convolutional neural network" OR "artificial intelligence" OR "detection" AND "stone volume"). Risk-of-bias(RoB) assessment was performed according to the Cochrane RoB tool, the Joanna Briggs Institute Checklist for nonrandomized studies and the Checklist for Artificial Intelligence in Medical Imaging(CLAIM).
Results: Twelve studies were selected for final review, including 3 multicenter and 9 single-center retrospective studies. 11 studies completed at least 50% of the CLAIM checkpoints and only one presented a high ROB. All included studies aimed to detect on kidney(5/12, 42%), ureter(2/12, 16%) or urinary(5/12, 42%) stones on Non-Contrast Computed Tomography(NCCT), but 42% intended to automate measurement. Stone distinction from vascular calcification interested 2 studies. All studies used AI machine learning network training and internal validation, but a single one provided an external validation. Trained networks achieved stone detection with sensitivity, specificity and accuracy rates ranging from 58,.7 to 100%, 68,.5 to 100% and 63 to 99,.95%, respectively. Detection Dice score ranged from 83% to 97%. A high correlation between manual and automated Stone Volume(r=0,.95) was noted. Differentiate distal ureteral stones and phleboliths seemed feasible.
Conclusions: Artificial Intelligence processes can achieve automated urinary stone detection from NCCT. Further studies should provide urinary stones detection coupled with phlebolith distinction and an external validation, and include anatomical abnormalities and urological foreign bodies (ureteral stent and nephrostomy tubes) cases.