Abstract
The purpose of this study was to profile elemental composition of calcium-based urinary stones using laser-induced breakdown spectroscopy (LIBS) and develop a machine learning model to distinguish recurrence-associated profiles by integrating elemental and clinical data. A total of 122 calcium-based stones (41 calcium oxalate, 11 calcium phosphate, 49 calcium oxalate/calcium phosphate, 8 calcium oxalate/uric acid, 13 calcium phosphate/struvite) were analyzed via LIBS. Elemental intensity ratios (H/Ca, P/Ca, Mg/Ca, Sr/Ca, Na/Ca, K/Ca) were calculated using Ca (396.847 nm) as reference. Clinical variables (demographics, laboratory and imaging results, recurrence status) were retrospectively collected. A back propagation neural network (BPNN) model was trained using four data strategies: clinical-only, spectral principal components (PCs), combined PCs plus clinical, and merged raw spectral plus clinical data. The performance of these four models was evaluated. Sixteen stone samples from other medical centers were used as external validation sets. Mg and Sr were detected in most of stones. Significant correlations existed among P, Mg, Sr, and K ratios. Recurrent patients showed elevated elemental ratios (p < 0.01), higher urine pH (p < 0.01), and lower stone CT density (p = 0.044). The BPNN model with merged spectral plus clinical data achieved optimal performance in classification (test set accuracy: 94.37%), significantly outperforming clinical-only models (test set accuracy: 73.37%). The results of external validation indicate that the model has good generalization ability. LIBS reveals ubiquitous Mg and Sr in calcium-based stones and elevated elemental ratios in recurrent cases. Integration of elemental profiles with clinical data enables high-accuracy classification of recurrence-associated profiles, providing insights for potential risk stratification in urolithiasis management.