Comprehensive analysis of 55,213 stones: understanding common morphological associations advances endoscopic stone...

To assess the prevalence and associations of urinary stone morphologies, focusing on their relevance for Endoscopic Stone Recognition and improving AI-assisted ESR (AESR) systems.

We analyzed a unique dataset comprising 55,213 stones classified by microscopic examination and Fourier transform infrared spectroscopy (FTIR) to assess the prevalence and associations of morphologies. We investigated the probabilities of observing a secondary morphology given a predominant morphology and the probabilities of observing a cross-sectional morphology given a surface morphology. Furthermore, we evaluated the performance of an AESR algorithm in detecting both pure and mixed stones across the six morphologies with a prevalence above 10% (Ia/Ib-Calcium oxalate monohydrate and IIa/IIb-Calcium oxalate dihydrate, IIIb-uric acid, and IVa-carbapatite), using a multi-label classification approach that integrates prior knowledge of common morphological associations.

This study provides key insights into common and rare urinary stone morphologies in a large cohort. The most frequent morphologies were Ia (55%), IVa (39%), and IIb (36%). We identified clinically relevant associations, notably Ia/Ib → IIb (28-30%), IIb → Ia (48%), and IIa → IVa (43%). We also showed that stone fragmentation often reveals deeper morphologies differing from the surface. In our experimental setup, AESR achieved mean balanced accuracies of 73% for surface images and 77% for section images, compared to 64% and 69% when morphological dependencies were ignored (Monte Carlo cross-validation over 10 random trials with 80% training, 10% validation, and 10% testing subsets).

Recognizing the prevalence and interrelationships of stone morphologies is essential for ESR. It improves both urologists' and AESR performance.