Purpose: Given the shortcomings of current stone burden characterization (maximum diameter or ellipsoid formulas), we sought to investigate the diagnostic accuracy and precision of a University of California, Irvine-developed artificial intelligence (AI) algorithm for determining stone volume determination.
Materials and methods: A total of 322 noncontrast CT scans were retrospectively obtained from patients with a diagnosis of urolithiasis. The largest stone in each noncontrast CT scan was designated the "index stone." The 3D volume of the index stone using 3D Slicer technology was determined by a validated reviewer; this was considered the "ground truth" volume. The AI-calculated index stone volume was subsequently compared with ground truth volume as well with the scalene, prolate, and oblate ellipsoid formulas estimated volumes.
Results: There was a nearly perfect correlation between the AI-determined volume and the ground truth (R=0.98). While the AI algorithm was efficient for determining the stone volume for all sizes, its accuracy improved with larger stone size. Moreover, the AI stone volume produced an excellent 3D pixel overlap with the ground truth (Dice score=0.90). In comparison, the ellipsoid formula-based volumes performed less well (R range: 0.79-0.82) than the AI algorithm; for the ellipsoid formulas, the accuracy decreased as the stone size increased (mean overestimation: 27%-89%). Lastly, for all stone sizes, the maximum linear stone measurement had the poorest correlation with the ground truth (R range: 0.41-0.82).
Conclusions: The University of California, Irvine AI algorithm is an accurate, precise, and time-efficient tool for determining stone volume. Expanding the clinical availability of this program could enable urologists to establish better guidelines for both the metabolic and surgical management of their urolithiasis patients.
Keywords: artificial intelligence; kidney stones; urolithiasis.