Table 4.
Summary of studies on the contribution of AI in the elucidation of stone disease chemistry and composition.
| Study | Objective | Study design | AI-based outcome | Comparator arm outcome |
|---|---|---|---|---|
| Dussol et al. [58] | Risk factors for calcium stones | Case-control | Classification accuracy between stone formers and controls: 74.4% | 75.8% |
| Dussol et al. [59] | Risk factors for calcium stones | Case-control | CaOx supersaturation and 24 h-urea for all men and women with a family history | No comparator |
| Kazemi and Mirroshandel [60] | Risk of nephrolithiasis | Cohort | Accuracy of 97.1% | Other classifiers with lower accuracy |
| Chen et al. [61] | Risk of forming renal stones of >2 cm | Cohort | AUC of 0.69 | AUC of 0.74 |
| Kavoussi et al. [62] | Prediction of 24 h urine abnormalities relevant for stone disease | Cohort | Higher accuracy in prediction of urine volume, uric acid, and natrium abnormalities | Higher accuracy in prediction of pH and citrate abnormalities |
| Caudarella et al. [63] | Risk of stone disease recurrence | Case-control | Accuracy of 88.8% | Accuracy of 67.5% |
| Chiang et al. [64] | Risk for stone disease | Case-control | Accuracy of 89% | Accuracy of 74% |
| Xiang et al. [65] | Identification of CaOx crystallization in urine sediment | Cross-sectional | Accuracy of 74% | Accuracy of 74% |
| Kletzmayr et al. [66] | Recognition of crystallization inhibition | Experimental | IP6 analogues inhibit effectively CaOx crystallization | No comparator |
| Kriegshauser et al. [67] | Stone composition by CT | Cross-sectional | Accuracy of 97% (UA instead of non-UA stones) and 72% among non-UA stones | Other multivariate models with lower performance |
| Kriegshauser et al. [68] | Stone composition by CT | Cross-sectional | Accuracy of 100% (UA instead of non-UA stones) and 88% among non-UA stones | Other multivariate models with lower performance |
| Zhang et al. [69] | Stone composition by CT | Cross-sectional | AUC of 0.965 (SD: 0.029) for UA instead of non-UA stones | Sensitivity of 94.4% and specificity of 93.7% for model using CT TA |
| Große Hokamp et al. [70] | Stone composition by CT | Cross-sectional | Accuracy of 91.1% on a per-voxel basis; accuracy of 87.1%–90.4% on independently tested acquisitions | No comparator |
| Tang et al. [71] | Stone composition by CT | Cross-sectional | Accuracy of 88.3% for COM instead of non-COM stones (AUC=0.933) | No comparator |
| Black et al. [72] | Stone composition by visual image | Cross-sectional | Prediction precision for each stone composition from 71.43% (struvite) to 95% (COM stones) | No comparator |
| Lopez et al. [73] | Stone composition by visual image | Cross-sectional | Precision of 93%–98%, depending on stone type | Other multivariate models with lower performance |
| El Beze et al. [74] | Stone composition by visual image | Cross-sectional | PPV of 96%–99%, depending on stone type | PPV of 88%–99%, depending on stone type |
| Ochoa-Ruiz et al. [75] | Stone composition by visual image | Cross-sectional | Overall precision of 97% | Overall precision of 96% |
| Mendez-Ruiz et al. [76] | Stone composition by visual image | Cross-sectional | Overall accuracy of 74.38% and 88.52%, depending on the image capturing method | Overall accuracy of 45% |
| Kim et al. [77] | Stone composition by visual image | Cross-sectional | AUC of 0.98–1.00, depending on stone type | Other multivariate models with lower performance |
| Fitri et al. [78] | Stone composition by microtomography | Cross-sectional | Overall accuracy of 99.59% | No comparator |
| Saçlı et al. [79] | Stone composition by dielectric properties | Cross-sectional | Overall accuracy of 98.17% | No comparator |
| Cui et al. [80] | Stone composition by Raman spectroscopy | Cross-sectional | Overall accuracy of 96.3% | No comparator |
| Onal and Tekgul [81] | Stone composition by smartphone microscopy | Cross-sectional | Overall accuracy of 88% | No comparator |
AI, artificial intelligence; AUC, area under the curve; CaOx, calcium oxalate; CT, computed tomography; TA, texture analysis; COM, calcium oxalate monohydrate; IP6, myoinositol hexakisphosphate; PPV, positive predictive value; UA, uric acid; SD, standard deviation.