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
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
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.
. [J]. Asian Journal of Urology, 2023, 10(3): 258-274.
Anastasios Anastasiadis,Antonios Koudonas,Georgios Langas,Stavros Tsiakaras,Dimitrios Memmos,Ioannis Mykoniatis,Evangelos N. Symeonidis,Dimitrios Tsiptsios,Eliophotos Savvides,Ioannis Vakalopoulos,Georgios Dimitriadis,Jean de la Rosette. Transforming urinary stone disease management by artificial intelligence-based methods: A comprehensive review. Asian Journal of Urology, 2023, 10(3): 258-274.
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
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
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
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
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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