Artificial neural networks in predicting optimum renal stone fragmentation by extracorporeal shock wave lithotripsy: a preliminary study

Using ensemble learning and hierarchical strategy to predict the outcomes of ESWL for upper ureteral stone treatment

Urinary tract stones are a common and frequently recurring medical issue. Accurately predicting the success rate after surgery can help avoid ineffective medical procedures and reduce unnecessary healthcare costs. This study collected data from patients with upper ureter stones who underwent extracorporeal shock wave lithotripsy, including cases of successful as well as unsuccessful stone removal after the first and second lithotripsy procedures, and constructed prediction systems for the outcomes of the first and second lithotripsy procedures. Features were extracted from three categories of information: patient characteristics, stone characteristics, and extracorporeal shock wave lithotripsy machine data, and additional features were created using Feature Creation. Finally, the impact of features on the models was analyzed using six methods to calculate feature importance. Our prediction model for the first lithotripsy, selected from among 43 methods and seven ensemble learning techniques, achieves an AUC of 0.91. For the second lithotripsy, the AUC reaches 0.76. The results indicate that the detailed and binary information provided by patients regarding their history of stone experiences contributes differently to the predictive accuracy of the first and second lithotripsy procedures. The prediction tool is available at https://predictor.isu.edu.tw/ks.

The use of intelligent analysis (IA) in determining the tactics of treating patients with nephrolithiasis

INTRODUCTION

The use of modern information technologies allows to increase confidence in the choice of a surgical treatment method of kidney stones, as well as to improve the quality of treatment due to an adequate combination of therapeutic techniques.

MATERIALS AND METHODS

In our study we analyzed the treatment results of 625 patients with kidney stones. We created a register with the information on more than 50 parameters for each patient. Each example had an output parameter representing a predefined treatment strategy (extracorporeal shock-wave lithotripsy [ESWL]-1, percutaneous nephrolithotomy [PCNL]-2, pyelolithotomy or nephrolithotomy-3). The initial database served as the basis for training the neural network estimation technique. The aim of our study was to assess the possibility of using neural network algorithms in choosing a method for surgical treatment of urolithiasis.

RESULTS

A prospective study was conducted to assess the clinical effectiveness of implementing the recommendations of the system. The average number of sessions in the group using the neural network assessment technique was 1.4. Residual fragments remained at the time of discharge in seven (15.6%) patients: four in the kidney, three in the lower third of the ureter "stone path." Inversion of therapeutic tactics-PCNL-was performed in four cases. The efficiency of the ESWL was 91.1%. The indicators of the ESWL in the comparison groups differed statistically significantly: in the second group, the efficiency was higher due to more stone fragmentation, with lower energy costs (the average number of sessions was 0.4 less).

CONCLUSION

The presented technique can be of help for a practicing urologist to choose the optimal treatment method for each patient, thereby minimizing the risk of early postoperative complications.

Transforming urinary stone disease management by artificial intelligence-based methods: A comprehensive review

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.

Artificial intelligence in endourology: emerging technology for individualized care

PURPOSE OF REVIEW

Artificial intelligence in medicine has allowed for efficient processing of large datasets to perform cognitive tasks that facilitate clinical decision-making, and it is an emerging area of research. This review aims to highlight the most pertinent and recent research in artificial intelligence in endourology, where it has been used to optimize stone diagnosis, support decision-making regarding management, predict stone recurrence, and provide new tools for bioinformatics research within endourology.

RECENT FINDINGS

Artificial neural networks (ANN) and machine learning approaches have demonstrated high accuracy in predicting stone diagnoses, stone composition, and outcomes of spontaneous stone passage, shockwave lithotripsy (SWL), or percutaneous nephrolithotomy (PCNL); some of these models outperform more traditional predictive models and existing nomograms. In addition, these approaches have been used to predict stone recurrence, quality of life scores, and provide novel methods of mining the electronic medical record for research.

SUMMARY

Artificial intelligence can be used to enhance existing approaches to stone diagnosis, management, and prevention to provide a more individualized approach to endourologic care. Moreover, it may support an emerging area of bioinformatics research within endourology. However, despite high accuracy, many of the published algorithms lack external validity and require further study before they are more widely adopted.

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

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.

A systematic review of the applications of Expert Systems (ES) and machine learning (ML) in clinical urology

BACKGROUND

Testing a hypothesis for 'factors-outcome effect' is a common quest, but standard statistical regression analysis tools are rendered ineffective by data contaminated with too many noisy variables. Expert Systems (ES) can provide an alternative methodology in analysing data to identify variables with the highest correlation to the outcome. By applying their effective machine learning (ML) abilities, significant research time and costs can be saved. The study aims to systematically review the applications of ES in urological research and their methodological models for effective multi-variate analysis. Their domains, development and validity will be identified.

METHODS

The PRISMA methodology was applied to formulate an effective method for data gathering and analysis. This study search included seven most relevant information sources: WEB OF SCIENCE, EMBASE, BIOSIS CITATION INDEX, SCOPUS, PUBMED, Google Scholar and MEDLINE. Eligible articles were included if they applied one of the known ML models for a clear urological research question involving multivariate analysis. Only articles with pertinent research methods in ES models were included. The analysed data included the system model, applications, input/output variables, target user, validation, and outcomes. Both ML models and the variable analysis were comparatively reported for each system.

RESULTS

The search identified n = 1087 articles from all databases and n = 712 were eligible for examination against inclusion criteria. A total of 168 systems were finally included and systematically analysed demonstrating a recent increase in uptake of ES in academic urology in particular artificial neural networks with 31 systems. Most of the systems were applied in urological oncology (prostate cancer = 15, bladder cancer = 13) where diagnostic, prognostic and survival predictor markers were investigated. Due to the heterogeneity of models and their statistical tests, a meta-analysis was not feasible.

CONCLUSION

ES utility offers an effective ML potential and their applications in research have demonstrated a valid model for multi-variate analysis. The complexity of their development can challenge their uptake in urological clinics whilst the limitation of the statistical tools in this domain has created a gap for further research studies. Integration of computer scientists in academic units has promoted the use of ES in clinical urological research.

An artificial intelligence-based clinical decision support system for large kidney stone treatment

A decision support system (DSS) was developed to predict postoperative outcome of a kidney stone treatment procedure, particularly percutaneous nephrolithotomy (PCNL). The system can serve as a promising tool to provide counseling before an operation. The overall procedure includes data collection and prediction model development. Pre/postoperative variables of 254 patients were collected. For feature vector, we used 26 variables from three categories including patient history variables, kidney stone parameters, and laboratory data. The prediction model was developed using machine learning techniques, which includes dimensionality reduction and supervised classification. A novel method based on the combination of sequential forward selection and Fisher's discriminant analysis was developed to reduce the dimensionality of the feature space and to improve the performance of the system. Multiple classifier scheme was used for prediction. The derived DSS was evaluated by running leave-one-patient-out cross-validation approach on the dataset. The system provided favorable accuracy (94.8%) in predicting the outcome of a treatment procedure. The system also correctly estimated 85.2% of the cases that required stent placement after the removal of a stone. In predicting whether the patient might require a blood transfusion during the surgery or not, the system predicted 95.0% of the cases correctly. The results are promising and show that the developed DSS could be used in assisting urologists to provide counseling, predict a surgical outcome, and ultimately choose an appropriate surgical treatment for removing kidney stones.

External validation of Imamura nomogram as a tool to predict preoperatively laser semi-rigid ureterolithotripsy outcomes

BACKGROUND

We aimed to validate Imamura nomogram for prediction of stone free rate in patients undergoing ureterolithotripsy (ULT).

METHODS

From January 2013 to June 2016, patients undergoing laser semi-rigid ULT were prospectively enrolled at our center. All patients were preoperatively assessed with clinical history, blood samples, uranalysis and non-contrast enhanced computed tomography (CT). Treatment efficacy was assessed 1 month later by non-contrast enhanced CT. ROC curve was used to evaluate the performance characteristics of Imamura nomogram.

RESULTS

Overall, we enrolled 275 patients. Median age was 55 years (IQR: 46/64), median length of stone was 9.8 mm (IQR: 7.5/12). Pyuria was detected in 6/275 (2.1%) patients. Stones were located at ureteropelvic junction in 55/275 (19%) patients, proximal ureter in 74/275 (26%) patients, middle and distal ureter in 66/275 (24%) patients and 82/275 (30%) patients, respectively. At 1-month follow-up, 209/275 (76%) patients were stone free. Imamura nomogram presented an AUC of 0.67 (95% CI: 0.580-0.761) for the prediction of stone free rate. At the best cut-off value of 75%, sensitivity was 76%, specificity was 55%, positive predictive value (PPV) was 83% and negative predictive value was 45%.

CONCLUSIONS

We firstly validated Imamura nomogram in a European cohort study. It proved a reasonable accuracy (area under curve: 0.67) and a good PPV (83%). Further studies should confirm our results to support the routine clinical use of Imamura nomogram as a tool to predict ULT outcomes.

A neural network - based algorithm for predicting stone - free status after ESWL therapy

OBJECTIVE

The prototype artificial neural network (ANN) model was developed using data from patients with renal stone, in order to predict stone-free status and to help in planning treatment with Extracorporeal Shock Wave Lithotripsy (ESWL) for kidney stones.

MATERIALS AND METHODS

Data were collected from the 203 patients including gender, single or multiple nature of the stone, location of the stone, infundibulopelvic angle primary or secondary nature of the stone, status of hydronephrosis, stone size after ESWL, age, size, skin to stone distance, stone density and creatinine, for eleven variables. Regression analysis and the ANN method were applied to predict treatment success using the same series of data.

RESULTS

Subsequently, patients were divided into three groups by neural network software, in order to implement the ANN: training group (n=139), validation group (n=32), and the test group (n=32). ANN analysis demonstrated that the prediction accuracy of the stone-free rate was 99.25% in the training group, 85.48% in the validation group, and 88.70% in the test group.

CONCLUSIONS

Successful results were obtained to predict the stone-free rate, with the help of the ANN model designed by using a series of data collected from real patients in whom ESWL was implemented to help in planning treatment for kidney stones.

Multiple adaptive neuro-fuzzy inference system with automatic features extraction algorithm for cervical cancer recognition

To date, cancer of uterine cervix is still a leading cause of cancer-related deaths in women worldwide. The current methods (i.e., Pap smear and liquid-based cytology (LBC)) to screen for cervical cancer are time-consuming and dependent on the skill of the cytopathologist and thus are rather subjective. Therefore, this paper presents an intelligent computer vision system to assist pathologists in overcoming these problems and, consequently, produce more accurate results. The developed system consists of two stages. In the first stage, the automatic features extraction (AFE) algorithm is performed. In the second stage, a neuro-fuzzy model called multiple adaptive neuro-fuzzy inference system (MANFIS) is proposed for recognition process. The MANFIS contains a set of ANFIS models which are arranged in parallel combination to produce a model with multi-input-multioutput structure. The system is capable of classifying cervical cell image into three groups, namely, normal, low-grade squamous intraepithelial lesion (LSIL) and high-grade squamous intraepithelial lesion (HSIL). The experimental results prove the capability of the AFE algorithm to be as effective as the manual extraction by human experts, while the proposed MANFIS produces a good classification performance with 94.2% accuracy.

Development of a nomogram for predicting the stone-free rate after transurethral ureterolithotripsy using semi-rigid ureteroscope

OBJECTIVES

To develop and to internally validate a novel nomogram for predicting the stone-free rate after transurethral ureterolithotripsy.

METHODS

A total of 412 patients with 534 ureteral stones were treated with transurethral ureterolithotripsy using semi-rigid ureteroscopes. Treatment efficacy was evaluated 3 months after the procedure. Multivariate stepwise logistic regression analysis was used to identify independent predictors of being stone-free in the model-building set. A total of 427 stones (80% of 534) were randomly allocated for identification and statistical analysis to build the model, and the remaining 107 (20%) were used for cross-validation. A nomogram for the stone-free rate was developed based on the final logistic regression model.

RESULTS

Stone length, number of stones, stone location and the presence of pyuria were independent factors related to the stone-free rate after transurethral ureterolithotripsy treatment, and these were used to develop a nomogram. In this nomogram, the area under the receiver operating characteristic curve was 0.7432 for the nomogram, 0.5641 for stone size, 0.5908 for the number of stones, 0.6594 for stone location and 0.6076 for pyuria. Validation using 20% of the data also achieved a reasonable predictive accuracy (area under the receiver operating characteristic curve = 0.682).

CONCLUSIONS

The first nomogram for predicting the stone-free rate after transurethral ureterolithotripsy was developed. It has a reasonable predictive accuracy, and in combination with extracorporeal shock wave lithotripsy nomograms, it might be useful for deciding treatment methods.

[The future of ESWL]

With the introduction of the Dornier HM3 lithotripter, the successful history of extracorporeal shock wave lithotripsy (ESWL) for noninvasive treatment of urinary stones began about 25 years ago. The development of newer lithotripters has not been able to improve clinical efficacy because the shock wave parameters specifically responsible for stone disintegration or tissue trauma and pain induction have not yet been identified. Actual research in lithotripter technology deals with modification of the focal point. The evolution of endoscopic procedures, ureterorenoscopy, and percutaneous nephrolithotomy took longer but was more successful in terms of clinical efficacy. Nowadays, ESWL or an endoscopic procedure is offered as a reasonable option for most urinary stone cases. Therefore, economic aspects and the surgeon's expertise will become greater factors when a procedure is chosen. ESWL, with or without anaesthesia, will be an inherent part of future treatment modalities for urinary stones.

Validation study of artificial neural network models for prediction of methicillin-resistant Staphylococcus aureus carriage

OBJECTIVE

Use of active surveillance cultures for methicillin-resistant Staphylococcus aureus (MRSA) for all patients admitted to the intensive care unit has been shown to reduce nosocomial transmission. However, the cost-effectiveness and the utility of implementing use of active surveillance cultures nationwide remain controversial. We sought to develop an artificial neural network (ANN) model that would predict the likelihood of MRSA colonization.

SETTING

Two acute care hospitals, one in Pittsburgh (hospital A) and one in Kaohsiung, Taiwan (hospital B).

METHODS

Nasal cultures were performed for all patients admitted to the hospitals. A total of 46 potential risk factors in hospital A and 86 potential risk factors in hospital B associated with MRSA colonization were assessed. Culture results were obtained; 75% of the data were used for training our ANN model, and the remaining 25% were used for validating our ANN model. The culture results were the "gold standard" for determining the accuracy of the model predictions.

RESULTS

The ANN model predictions were accurate 95.2% of the time for hospital A (sensitivity, 94.3%; specificity, 96.0%) and 94.2% of the time for hospital B (sensitivity, 96.6%; specificity, 91.8%), integrating all potential risk factors into the model. Only 17 potential risk factors were needed for the hospital A ANN model (accuracy, 90.9%; sensitivity, 98.5%; specificity, 83.4%), and only 20 potential risk factors were needed for the hospital B ANN model (accuracy, 90.5%; sensitivity, 96.6%; specificity, 84.3%), if the minimal risk factor method was used. Cross-validation analysis showed an average accuracy of 85.6% (sensitivity, 91.3%; specificity, 80.0%).

CONCLUSION

Our ANN model can be used to predict with an accuracy of more than 90% which patients carry MRSA. The false-negative rates were significantly lower than the false-positive rates in the ANN predictions, which can serve as a safety buffer in case of patient misclassification.

Artificial neural networks in urolithiasis

PURPOSE OF REVIEW

The management of urolithiasis is a clinical challenge worldwide which may result in difficulty in diagnosis, treatment and prevention of recurrence. Artificial neural networks (ANNs) are well described adjuncts to many aspects of clinical urological practice. We review literature published in on-line Medline-citable English language journals to assess whether ANNs are useful in clinician-led decision-making processes in urolithiasis.

RECENT FINDINGS

Studies have examined the role of ANNs in prediction of stone presence and composition, spontaneous passage, clearance and regrowth after treatment. These reports suggest that ANNs can identify important predictive variables and accurately predict treatment outcome.

SUMMARY

Although well described in general urological practice, there is comparatively little research into the role of ANNs in urolithiasis. Initial results appear promising; however, further prospective studies are necessary to determine if this mode of analysis is superior to standard statistical predictive methods.

Can we improve the prediction of stone-free status after extracorporeal shock wave lithotripsy for ureteral stones? A neural network or a statistical model?

PURPOSE

We evaluated whether an artificial neural network (ANN) can improve the prediction of stone-free status after extracorporeal shock wave lithotripsy (ESWL) (Dornier Medical Systems, Inc., Marietta, Georgia) for ureteral stones compared to a logistic regression (LR) model.

MATERIALS AND METHODS

Between February 1989 and December 1998, 984 patients with ureteral stones, including 780 males and 204 females with a mean age +/- SD of 40.85 +/- 10.33 years, were treated with ESWL. Stone-free status at 3 months was determined by urinary tract plain x-ray and excretory urography. Of all patients 919 (93.3%) were free of stones. The impact of 10 factors on stone-free status was studied using an LR model and ANN. These factors were patient age and sex, renal anatomy, stone location, side, number, length and width, whether stones were de novo or recurrent, and stent use. An LR model was constructed and ANN was trained on 688 randomly selected patients (70%) to predict stone-free status at 3 months. The 10 factors were used as covariates in the LR model and as input parameters to ANN. Performance of the trained net and developed logistic model was evaluated in the remaining 296 patients (30%), who served as the test set. The sensitivity (percent of correctly predicted stone-free cases), specificity (percent of correctly predicted nonstonefree cases), positive predictive value, overall accuracy and average classification rate of the 2 techniques were compared. Relevant variables influencing the construction of the 2 models were compared.

RESULTS

Evaluating the performance of the LR and ANN models on the test set revealed a sensitivity of 100% and 77.9%, a specificity of 0.0% and 75%, a positive predictive value of 93.2% and 97.2%, an overall accuracy of 93.2% and 77.7%, and an average classification rate of 50% and 76.5%, respectively. LR failed to predict any nonstone free cases. LR and ANN identified stone location and stent use as important factors in determining the outcome, while ANN also identified stone length and width as influential factors.

CONCLUSIONS

ANN and LR could predict adequately those who would be stone-free after ESWL for ureteral stones. The neural network has a higher ability to predict those who fail to respond to ESWL.