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| Transforming urinary stone disease management by artificial intelligence-based methods: A comprehensive review |
Anastasios Anastasiadisa,Antonios Koudonasa,Georgios Langasa,Stavros Tsiakarasa,*( ),Dimitrios Memmosa,Ioannis Mykoniatisa,Evangelos N. Symeonidisa,Dimitrios Tsiptsiosb,Eliophotos Savvidesc,Ioannis Vakalopoulosa,Georgios Dimitriadisa,Jean de la Rosetted
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a 1st Department of Urology, Aristotle University of Thessaloniki, School of Medicine, “G.Gennimatas” General Hospital, Thessaloniki, Greece
b Neurology Department, Democritus University of Thrace, Alexandroupolis, Greece
c Department of Urology, Main Kinzig Kliniken, Gelnhausen, Germany
d Department of Urology, Istanbul Medipol Mega University Hospital, Istanbul, Turkey |
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Abstract Objective To provide a comprehensive review on the existing research and evidence regarding artificial intelligence (AI) applications in the assessment and management of urinary stone disease.
Methods A comprehensive literature review was performed using PubMed, Scopus, and Google Scholar databases to identify publications about innovative concepts or supporting applications of AI in the improvement of every medical procedure relating to stone disease. The terms ‘‘endourology’’, ‘‘artificial intelligence’’, ‘‘machine learning’’, and ‘‘urolithiasis'’ were used for searching eligible reports, while review articles, articles referring to automated procedures without AI application, and editorial comments were excluded from the final set of publications. The search was conducted from January 2000 to September 2023 and included manuscripts in the English language.
Results A total of 69 studies were identified. The main subjects were related to the detection of urinary stones, the prediction of the outcome of conservative or operative management, the optimization of operative procedures, and the elucidation of the relation of urinary stone chemistry with various factors.
Conclusion AI represents a useful tool that provides urologists with numerous amenities, which explains the fact that it has gained ground in the pursuit of stone disease management perfection. The effectiveness of diagnosis and therapy can be increased by using it as an alternative or adjunct to the already existing data. However, little is known concerning the potential of this vast field. Electronic patient records, containing big data, offer AI the opportunity to develop and analyze more precise and efficient diagnostic and treatment algorithms. Nevertheless, the existing applications are not generalizable in real-life practice, and high-quality studies are needed to establish the integration of AI in the management of urinary stone disease.
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Received: 12 September 2022
Available online:
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Corresponding Authors:
Stavros Tsiakaras
E-mail: drstavros90@gmail.com
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Subsets of artificial intelligence with emergent role in stone disease management.
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Flowchart of the literature selection process for articles.
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| Study | Objective | Study design | AI-based outcome | Comparator arm outcome | | Li et al. [13] | Detection of urinary stones by CT | Cross-sectional | Detection accuracy of 99.95% | Other algorithms with lower performance | | Parakh et al. [14] | Detection of urinary stones by CT | Cross-sectional | High accuracy in stone detection (AUC of 0.954) | Other algorithms with lower performance | | L?ngkvist et al. [15] | Detection of urinary stones by CT | Cross-sectional | Optimized accuracy with an AUC of 0.9971 | No comparator | | Caglayan et al. [16] | Detection of urinary stones by CT | Cross-sectional | Accuracy of 63%-93%, depending on imaging plane and stone size class | No comparator | | Jendeberg et al. [17] | Differentiation of ureteral stones and pelvic phleboliths by CT | Cross-sectional | Accuracy of 92% | Mean radiologist accuracy: 86%; majority vote accuracy: 93% | | De Perrot et al. [18] | Differentiation of ureteral stones and pelvic phleboliths by CT | Cross-sectional | Overall accuracy of 85.1% (AUC of 0.902) | Other algorithms with lower performance | | Chak et al. [19] | Detection of urinary stones by CT | Cross-sectional | Accuracy of 95%-99%, depending on the number of features used by the algorithm | No comparator | | G P et al. [20] | Detection of urinary stones by CT | Cross-sectional | Accuracy of 96.82% | No comparator | | Elton et al. [21] | Detection of urinary stones by CT | Cross-sectional | High accuracy in stone detection (AUC of 0.95) | No comparator | | Krishna et al. [22] | Differentiation of renal stones and renal cysts by US | Cross-sectional | Accuracy of 98.1% | No comparator | | Balamurugan and Arumugam [23] | Differentiation of renal stones among other abnormalities in US | Cross-sectional | Accuracy of 95.83% | Other algorithms with lower performance | | Selvarani and Rajendran [24] | Detection of renal stones by US | Cross-sectional | Accuracy of 98.8% | Other algorithms with lower performance | | Viswanath et al. [25] | Detection of renal stones by US | Cross-sectional | Accuracy of 98.9% | Other algorithms with lower performance | | Akkasaligar and Biradar [26] | Detection of renal stones by US | Cross-sectional | Accuracy of 96.8% | No comparator | | Verma et al. [27] | Detection of renal stones by US | Cross-sectional | Accuracy of 89% | Other algorithms with lower performance | | Kobayashi et al. [28] | Detection of radio-opaque urinary stones in KUB X-ray images | Cross-sectional | Sensitivity and PPV of 89.6% and 56.9% for the kidney, 92.5% and 87.6% for the proximal ureter, 59.1% and 50% for the mid-ureter, 80% and 55.8% for the distal ureter | No comparator | | Aksakalli et al. [29] | Detection of radio-opaque urinary stones in KUB X-ray images | Cross-sectional | Precision of 78.4% | Other algorithms with lower performance |
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Summary of studies regarding AI in the detection of stone disease.
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| Study | Objective | Study design | AI-based outcome | Comparator arm outcome | | Cummings et al. [30] | Prediction of SSP | Case-control | Accuracy of 76% | No comparator | | Dal Moro et al. [31] | Prediction of SSP | Case-control | 84.5% sensitivity and 86.9% specificity | Other algorithms with lower performance | | Solakhan et al. [32] | Prediction of SSP | Case-control | Accuracy of 92.8% | Other algorithms with lower performance | | Park et al. [33] | Prediction of SSP | Case-control | AUCs of 0.859 (stones of <5 mm) and 0.881 (stones of 5-10 mm) | AUC of 0.847 (stones of <5 mm) and 0.817 (stones of 5 mm-10 mm) | | Poulakis et al. [34] | Prediction of lower pole clearance after ESWL | Case-control | Accuracy of 92% | No comparator | | Gomha et al. [35] | Prediction of clearance after ESWL for ureteral stones | Case-control | Accuracy of 77.7% | Accuracy of 93.2% | | Moorthy and Krishnan [36] | Prediction of renal stone fragmentation after ESWL | Case-control | Accuracy of 90% | No comparator | | Choo et al. [37] | Prediction of clearance after ESWL for ureteral stones | Case-control | Accuracy of 92.29% | No comparator | | Seckiner et al. [38] | Prediction of clearance after ESWL for renal stones | Case-control | Accuracy of 88.70% | No comparator | | Mannil et al. [39] | Prediction of renal stones fragmentation after ESWL | Case-control | AUC of 0.85 | Other algorithms with lower performance | | Yang et al. [40] | Prediction of clearance after ESWL for renal or upper ureter stones | Case-control | AUC of 0.85 for stone-free status in an interval of 4 weeks; AUC of 0.78 for stone-free status after single session ESWL | Other algorithms with similar performance | | Tsitsiflis et al. [41] | Prediction of complications after ESWL for renal or ureteral stones | Case-control | Accuracy of 81.43% | No comparator | | Handa et al. [42] | Quantification of ESWL-induced renal injury by MRI | Experimental | Strong correlation between model prediction and morphology (r=0.9691) | No comparator | | Aminsharifi et al. [43] | Prediction of multiple outcomes after PCNL | Case-control | Accuracy of 91.8%, 83% regarding stone clearance and need for blood transfusion; AUC of 0.915 for stone clearance | AUCs of 0.615 and 0.621 for stone clearance according to GSS and CROES nomograms | | Shabaniyan et al. [44] | Prediction of multiple outcomes after PCNL | Case-control | Accuracy of 94.8% in prediction of the procedures‘ outcome, 85.2% accuracy in predicting the need for stent placement and 95% in predicting blood transfusion | Multiple decision support systems achieving higher performances in different parameters | | Aminsharifi et al. [45] | Prediction of multiple outcomes after PCNL | Case-control | Accuracy of 82.8%, 92.5%-98.2%, 81.1%, and 85.8% for stone clearance, need for a second procedure, stent insertion by urine extravasation, and blood transfusion | No comparator | | Geraghty et al. [46] | Prediction of multiple outcomes after PCNL | Case-control | Multiple classification models tested, highest accuracy of 99% and AUCs of 0.99-1.00 achieved for need for transfusion and infectious complications | No comparator | | Zhao et al. [47] | Prediction of stone clearance after PCNL | Case-control | AUC of 0.879 | AUC of 0.800 for GSS; AUC of 0.844 for S.T.O.N.E. score | | Chen et al. [48] | Prediction of sepsis after fURS or PCNL for proximal ureteral stones | Case-control | AUC of 0.874 for DNN model | AUC of 0.783 for LASSO model |
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Summary of studies regarding AI in the prediction of management outcomes.
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| Study | Objective | Study design | AI-based outcome | Comparator arm outcome | | Hamid et al. [49] | Optimization of ESWL protocol | Cross-sectional | Accuracy of 75% for predicting shockwave number and 100% for predicting patients needing shockwave number beyond protocol | No comparator | | Goyal et al. [50] | Optimization of ESWL protocol | Cross-sectional | Correlation coefficients for power level and shockwave number of 0.8343 and 0.9329, respectively | Correlation coefficients for power level and shockwave number 0.0195 and 0.5726, respectively | | Mannil et al. [51] | Optimization of ESWL protocol | Experimental | AUC of 0.838 in the prediction of fragmentation with less than 72 shockwaves | Other multivariate models with lower performance | | Chen et al. [52] | Optimization of ESWL protocol | Cross-sectional | Prediction accuracy values for power level, shockwave rate of 98.8%, 98.1%, respectively | Other multivariate models with lower performance | | Muller et al. [53] | Optimization of ESWL protocol | Cross-sectional | Shockwave hit rate of 75.3% | Shockwave hit rate of 55.2% | | Taguchi et al. [54] | Optimization of PCNL puncture | Experimental | Puncture success rate of 100%; puncture time of 35 s | Puncture success rate of 70.6%; puncture time of 46 s | | Wang et al. [55] | Optimization of PCNL puncture | Experimental | Average recognition precision of 79% (SE: 4%) for cortex, 85% (SE: 6%) for medulla, and 91% (SE: 5%) for calyx | No comparator | | Li et al. [56] | Optimization of PCNL puncture | Cross-sectional | ANN model achieved a better localization and puncture method selection compared to the MVRA model and the surgeon's experience | No comparator | | Jeong et al. [57] | Optimization of RIRS safety profile | Experimental | Recognition of tissue exposure to laser energy with accuracy of 95% and latency time of 0.5 s | No comparator |
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Summary of studies regarding the contribution of AI in the optimization of the operative procedure.
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| 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 | | Gro?e 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 | | Sa?l? 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 |
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Summary of studies on the contribution of AI in the elucidation of stone disease chemistry and composition.
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