Machine learning prediction of stone-free success in patients with urinary stone after treatment of shock wave lithotripsy

Yang, Seung Woo; Hyon, Yun Kyong; Na, Hyun Seok; Jin, Long; Lee, Jae Geun; Park, Jong Mok; Lee, Ji Yong; Shin, Ju Hyun; Lim, Jae Sung; Na, Yong Gil; Jeon, Kiwan; Ha, Tae Youl; Kim, Jinbum; Song, Ki Hak

doi:10.1186/s12894-020-00662-x

Cited by 23 publications

(23 citation statements)

References 25 publications

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“…Machine learning algorithms have their own advantages in data analysis, and mainly consist of two types, supervised and unsupervised. As supervised was used to validate unknown domain insights and not recognized before, we selected two algorithms based on decision trees, random forest and XGBoost, and SVM represented not based on decision trees [13]. Random forest has great success in many cases because of its remarkably accurate nal majority votes [25], while XGBoost could replace the regression analysis due to the minimal gradient descent loss [26].…”

Section: Discussionmentioning

confidence: 99%

“…Researchers used these computer algorithms based on machine learning to predict the stone-free rate, the stone composition, and complications in kidney stone disease [13][14][15], and provided matched insights to domain knowledge on effective and in uential factors disease outcomes. Therefore, these new algorithms may help improve the prediction of transfusion receiving PCNL, but available data is limited.…”

Section: Introductionmentioning

confidence: 99%

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Application of Machine-Learning Models in Predicting Transfusion Among Complex Renal Stones Patients Receiving Percutaneous Nephrolithotomy：A Retrospective Study

Yang¹,

Zhi²,

Wang³

et al. 2022

Preprint

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To predict and compare machine-learning models with logistic regression (LR) for transfusion requirement among complex renal stones patients receiving PCNL. The data of 513 patients with complex renal stones receiving PCNL between January 2016 and December 2021 from 2 centers were analyzed retrospectively and reviewed the pre-, intra-, and post-operation factors. We used 80% training set and 20% test set for the predictions of transfusion by LR and machine-learning algorithms, random forest (RF), support vector machine (SVM), and extreme gradient boosting trees (XGBoost), and compared the prediction accuracies, precisions, and area under curve (AUC) values of receiving operating characteristic (ROC) curves in each model. In our study, the prediction accuracies of RF (Accuracy = 0.974), SVM (Accuracy = 0.968) and XGBoost (Accuracy = 0.968) models all outperformed LR (Accuracy = 0.948), and the precision of RF (Precision = 0.818) was higher than XGBoost (Precision = 0.8), SVM (Precision = 0.8) and LR (Precision = 0.8). Moreover, RF (AUC = 0.962), SVM (AUC = 0.786) and XGBoost (AUC = 0.867) offered higher AUC values than LR (AUC = 0.77). The RF confirmed size of tract, number of tracts, and puncture correctness as significant risk factors, which was the same as LR model, while the size of tract was the main factor in XGBoost. All machine-learning algorithms yielded better predictions than LR for transfusion among PCNL patients. The importance of machine learning algorithm can give matched insights to domain knowledge on effective and influential factors for transfusion requirement.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of Machine-Learning Models in Predicting Transfusion Among Complex Renal Stones Patients Receiving Percutaneous Nephrolithotomy：A Retrospective Study

Yang¹,

Zhi²,

Wang³

et al. 2022

Preprint

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“…Yang et al used ML methods such as random forest (RF), extreme gradient boosting trees (XGboost), and light gradient boosting method (LightGBM) to predict the success rate of ESWL and also assess the factors affecting the outcomes using a dataset of 358 patients in the ratio of 80:20 as training and test dataset. In predictions for stone-free, LightGBM yielded the best accuracy (87.9%) with AUC 0.84-0.85 and sensitivity and specificity of 0.74-0.78 and 0.92-0.93 respectively [34].…”

Section: Extracorporeal Shockwave Lithotripsy (Eswl)mentioning

confidence: 97%

The Ascent of Artificial Intelligence in Endourology: a Systematic Review Over the Last 2 Decades

et al. 2021

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Purpose of Review To highlight and review the application of artificial intelligence (AI) in kidney stone disease (KSD) for diagnostics, predicting procedural outcomes, stone passage, and recurrence rates. The systematic review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) checklist. Recent Findings This review discusses the newer advancements in AI-driven management strategies, which holds great promise to provide an essential step for personalized patient care and improved decision making. AI has been used in all areas of KSD including diagnosis, for predicting treatment suitability and success, basic science, quality of life (QOL), and recurrence of stone disease. However, it is still a research-based tool and is not used universally in clinical practice. This could be due to a lack of data infrastructure needed to train the algorithms, wider applicability in all groups of patients, complexity of its use and cost involved with it. Summary The constantly evolving literature and future research should focus more on QOL and the cost of KSD treatment and develop evidence-based AI algorithms that can be used universally, to guide urologists in the management of stone disease.

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“…In terms of urolithiasis treatment, the postoperative outcomes of percutaneous nephrolithotomy were predicted using Fisher discriminant analysis [ 8 ] and support vector machine [ 9 ]. Similarly, the stone-free status after shockwave lithotripsy has been obtained using clinical information and CT image with an artificial neural network [ 10 ] and a decision tree method [ 11 , 12 ]. There also has been an attempt to predict the stone-free rate (SFR) prior to RIRS with the R.I.R.S scoring system and a statistical approach [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

A warning system for urolithiasis via retrograde intrarenal surgery using machine learning: an experimental study

Jeong

Chang

Lee³

et al. 2022

BMC Urol

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Background To develop a warning system that can prevent or minimize laser exposure resulting in kidney and ureter damage during retrograde intrarenal surgery (RIRS) for urolithiasis. Our study builds on the hypothesis that shock waves of different degrees are delivered to the hand of the surgeon depending on whether the laser hits the stone or tissue. Methods A surgical environment was simulated for RIRS by filling the body of a raw whole chicken with water and stones from the human body. We developed an acceleration measurement system that recorded the power signal data for a number of hours, yielding distinguishable characteristics among three different states (idle state, stones, and tissue–laser interface) by conducting fast Fourier transform (FFT) analysis. A discrete wavelet transform (DWT) was used for feature extraction, and a random forest classification algorithm was applied to classify the current state of the laser-tissue interface. Results The result of the FFT showed that the magnitude spectrum is different within the frequency range of < 2500 Hz, indicating that the different states are distinguishable. Each recorded signal was cut in only 0.5-s increments and transformed using the DWT. The transformed data were entered into a random forest classifier to train the model. The test result was only measured with the dataset that was isolated from the training dataset. The maximum average test accuracy was > 95%. The procedure was repeated with random signal dummy data, resulting in an average accuracy of 33.33% and proving that the proposed method caused no bias. Conclusions Our monitoring system receives the shockwave signals generated from the RIRS urolithiasis treatment procedure and generates the laser irradiance status by rapidly recognizing (in 0.5 s) the current laser exposure state with high accuracy (95%). We postulate that this can significantly minimize surgeon error during RIRS.

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Machine learning prediction of stone-free success in patients with urinary stone after treatment of shock wave lithotripsy

Cited by 23 publications

References 25 publications

Application of Machine-Learning Models in Predicting Transfusion Among Complex Renal Stones Patients Receiving Percutaneous Nephrolithotomy：A Retrospective Study

Application of Machine-Learning Models in Predicting Transfusion Among Complex Renal Stones Patients Receiving Percutaneous Nephrolithotomy：A Retrospective Study

The Ascent of Artificial Intelligence in Endourology: a Systematic Review Over the Last 2 Decades

A warning system for urolithiasis via retrograde intrarenal surgery using machine learning: an experimental study

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