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

Effect of acupressure on pain among older female patients undergoing extracorporeal shock wave lithotripsy: A randomized controlled clinical trial

BACKGROUND AND AIM

Urinary stones are highly prevalent among older people. Extracorporeal lithotripsy is one of the commonly used treatment methods, but it causes pain. Acupressure is a non-pharmacological therapeutic method that is effective in relieving pain among patients with various health conditions. This study aimed to investigate the effect of acupressure on pain among female older people undergoing extracorporeal lithotripsy.

METHOD

This randomized controlled clinical trial was conducted on 66 older female patients undergoing extracorporeal lithotripsy. They were enrolled in the study through convenient sampling and were assigned to the intervention and control groups through the block randomization method. The intervention group underwent acupressure for 16 min which was repeated twice with an interval of 20 min, but the control group received only touch without any pressure for the same period. The McGill Pain Questionnaire and Visual Analogue Scale were completed 60 min before the intervention and immediately after lithotripsy.

FINDINGS

Before the intervention, no statistically significant difference in the quality and intensity of pain between the two groups was observed (p > 0.05). However, after acupressure, the mean scores of quality and intensity of pain decreased significantly (p < 0.001) in the intervention group compared with the control group.

CONCLUSION

Acupressure as a complementary and alternative medicine can reduce pain and suffering among older people undergoing extracorporeal lithotripsy. It can be included in the routine therapeutic measures for relieving pain and suffering during noninvasive methods for older people and reducing their need for medication use and avoiding related pharmacological side effects.

Surgical Artificial Intelligence: Endourology

Endourology is ripe with information that includes patient factors, laboratory tests, outcomes, and visual data, which is becoming increasingly complex to assess. Artificial intelligence (AI) has the potential to explore and define these relationships; however, humans might not be involved in the input, analysis, or even determining the methods of analysis. Herein, the authors present the current state of AI in endourology and highlight the need for urologists to share their proposed AI solutions for reproducibility outside of their institutions and prepare themselves to properly critique this new technology.

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.

Surgical outcomes and postoperative hemorrhage risk of percutaneous nephrolithotomy (PCNL) for deer horn shaped stones analyzed by Lasso regression

OBJECTIVE

To predict surgical outcomes and postoperative hemorrhage risk for percutaneous nephrolithotomy (PCNL) in cases of staghorn-shaped stones using lasso regression.

METHODS

We collected data from 104 patients with staghorn-shaped stones treated with PCNL between January 2019 and December 2022 at the Department of Urology Surgery, the Third People's Hospital of Gansu Province. Medical history, stone-related parameters, and lab test data were collected. Patients were categorized into stone clearance or residual groups based on postoperative stone status, and bleeding or non-bleeding groups based on post-surgery blood transfusion. The lasso model's predictive ability for post-PCNL Stone Free Rate (SFR) and hemorrhage risk was evaluated using ROC curves. The lasso model's predictive performance for post-PCNL SFR was compared to the S.T.O.N.E. score.

RESULTS

Overall stone clearance rate was 59.29%. The lasso model identified hypertension history, calyx count at stone location, prior calyx surgeries, age, operation duration, and pre-op creatinine level as SFR predictors. The AUC of lasso model (0.867) significantly surpassed the S.T.O.N.E. model (0.748) (P=0.006) in predicting post-PCNL SFR. In addition, the AUC of lasso model in predicting the risk of postoperative bleeding was 0.779, suggesting an ability in the prediction of bleeding occurrence.

CONCLUSION

A predictive model utilizing lasso algorithm was successfully established. It effectively predicts stone clearance rate and bleeding risk after PCNL for staghorn shaped kidney stones.

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.

Theranostic roles of machine learning in clinical management of kidney stone disease

Kidney stone disease (KSD) is a common illness caused by deposition of solid minerals formed inside the kidney. The disease prevalence varies, based on sociodemographic, lifestyle, dietary, genetic, gender, age, environmental and climatic factors, but has been continuously increasing worldwide. KSD is a highly recurrent disease, and the recurrence rate is about 11% within two years after the stone removal. Recently, machine learning has been widely used for KSD detection, stone type prediction, determination of appropriate treatment modality and prediction of therapeutic outcome. This review provides a brief overview of KSD and discusses how machine learning can be applied to diagnostics, therapeutics and prognostics in clinical management of KSD for better therapeutic outcome.

Clinical Applications of Machine Learning for Urolithiasis and Benign Prostatic Hyperplasia: A Systematic Review

INTRODUCTION

Previous systematic reviews related to machine learning (ML) in urology often overlooked the literature related to endourology. Therefore, we aim to conduct a more focused systematic review examining the use of ML algorithms for benign prostatic hyperplasia (BPH) or urolithiasis. In addition, we are the first group to evaluate these articles using the STREAM-URO framework.

METHODS

Searches of MEDLINE, Embase, and the Cochrane CENTRAL databases were conducted from inception through July 12, 2021. Keywords included those related to ML, endourology, urolithiasis, and BPH. Two reviewers screened the citations that were eligible for title, abstract and full-text screening, with conflicts resolved by a third reviewer. Two reviewers extracted information from the studies, with discrepancies resolved by a third reviewer. The data collected was then qualitatively synthesized by consensus. Two reviewers evaluated each article according to the STREAM-URO checklist with discrepancies resolved by a third reviewer.

RESULTS

After identifying 459 unique citations, 63 articles were retained for data extraction. Most articles consisted of tabular (n=32) and computer vision (n=23) tasks. The two most common problem types were classification (n=40) and regression (n=12). In general, most studies utilized neural networks as their ML algorithm (n=36). Among the 63 studies retrieved, 58 were related to urolithiasis and five focused on BPH. The urolithiasis studies were designed for outcome prediction (n=20), stone classification (n=18), diagnostics (n=17), and therapeutics (n=3). The BPH studies were designed for outcome prediction (n=2), diagnostics (n=2), and therapeutics (n=1). On average, the urolithiasis and BPH articles met 13.8 (SD 2.6), and 13.4 (4.1) of the 26 STREAM-URO framework criteria, respectively.

CONCLUSIONS

The majority of the retrieved studies successfully helped with outcome prediction, diagnostics, and therapeutics for both urolithiasis and BPH. While ML shows great promise in improving patient care, it is important to adhere to the recently developed STREAM-URO framework to ensure the development of high-quality ML studies.

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

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.

Role of Multi-Detector Computed Tomography Indices in Predicting Extracorporeal Shockwave Lithotripsy Outcome in Patients With Nephrolithiasis

Background

Nephrolithiasis is one of the most common renal pathologies and is routinely encountered in daily practice. Non-contrast computed tomography (NCCT) is the gold standard diagnostic imaging modality for urolithiasis. The role of HU (Hounsfield units) in calculus as a predictor of extracorporeal shock wave lithotripsy (ESWL) has been studied in the past. This study aims to evaluate the role of HU value and various other NCCT indices in predicting the outcome of ESWL.

Material and methods

This was a prospective observational study that included 45 patients suffering from nephrolithiasis who underwent NCCT-KUB (kidney, ureter, and bladder) followed by ESWL. The NCCT indices were evaluated and correlated with the outcome of ESWL. NCCT-KUB was performed using multidetector SIEMENS® SOMATOM EMOTION 16-slice CT scanner (SIEMENS, Munich, Germany).

Results

In our study, the HU value turned out to be a statistically significant predictor of ESWL success (p <0.05), and the renal pelvis also proved to be a good prognostic indicator for ESWL success. The cut-off value of <1179 HU favored a successful outcome of ESWL, while if >1179 HU, ESWL is likely to fail. Hence, the successful outcome of ESWL is inversely proportional to the HU value. Renal pelvic calculi (n=14) showed a 100% success rate, which was better than all other calculus locations (p<0.05). However, the rest of the indices did not show any statistical significance.

Conclusion

Multi-detector NCCT-KUB indices can help in the selection of patients with a good prognosis for ESWL, which will prevent the patient from undergoing undesired invasive procedures.

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 use and applicability of machine learning algorithms in predicting the surgical outcome for patients with benign prostatic enlargement. Which model to use?

OBJECTIVES

Artificial intelligence (AI) is increasingly used in medicine, but data on benign prostatic enlargement (BPE) management are lacking. This study aims to test the performance of several machine learning algorithms, in predicting clinical outcomes during BPE surgical management.

METHODS

Clinical data were extracted from a prospectively collected database for 153 men with BPE, treated with transurethral resection (monopolar or bipolar) or vaporization of the prostate. Due to small sample size, we applied a method for increasing our dataset, Synthetic Minority Oversampling Technique (SMOTE). The new dataset created with SMOTE has been expanded by 453 synthetic instances, in addition to the original 153. The WEKA Data Mining Software was used for constructing predictive models, while several appropriate statistical measures, like Correlation coefficient (R), Mean Absolute Error (MAE), Root Mean-Squared Error (RMSE), were calculated with several supervised regression algorithms - techniques (Linear Regression, Multilayer Perceptron, SMOreg, k-Nearest Neighbors, Bagging, M5Rules, M5P - Pruned Model Tree, and Random forest).

RESULTS

The baseline characteristics of patients were extracted, with age, prostate volume, method of operation, baseline Qmax and baseline IPSS being used as independent variables. Using the Random Forest algorithm resulted in values of R, MAE, RMSE that indicate the ability of these models to better predict % Qmax increase. The Random Forest model also demonstrated the best results in R, MAE, RMSE for predicting % IPSS reduction.

CONCLUSIONS

Machine Learning techniques can be used for making predictions regarding clinical outcomes of surgical BPRE management. Wider-scale validation studies are necessary to strengthen our results in choosing the best model.

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.

The effect of stone and patient characteristics in predicting extra-corporal shock wave lithotripsy success rate: A cross sectional study

Introduction

We determine the effect of patient characteristics (age, sex, and body mass index BMI) and stone characteristics (density, location, and size) by non-contrast computed tomography of the kidneys, ureters, and bladder (CT-KUB) in predicting the success of extracorporeal shock wave lithotripsy (ESWL) in the treatment of kidney and ureteric stones. We present this study to further enrich the knowledge of physicians towards the effect of different patient characteristics upon predicting extra-corporal shock wave lithotripsy success rates.

Methods

We evaluated 155 patients who received ESWL for renal and ureteric stone measuring 3–20 mm (mm), over a 3-month period. The stone size in millimeters, density in Hounsfield units (HU) and its location was determined on pre-treatment CT-KUB. ESWL was successful if post-treatment residual renal stone fragments were ≤3 mm and for ureteric stones should be totally cleared.

Results

The overall success of ESWL treatment was observed in 65.8% of the 155 patients. There was no significant difference seen when the effect of patients age, sex and BMI were studied with ESWL outcome with P values were 0.155, 0.101 and 0.415 respectively. Also, stone location either in the kidney or ureter has no statistically significant effect on ESWL response rate. while stone density and size determined on CT KUB have statistically significant effect on the success rate of ESWL with a P-value of 0.002 and 0.000 respectively.

Conclusions

This study shows that determination of stone density and stone size on CT KUB pre ESWL can help to predict the outcome of ESWL. We propose that stone density <500 HU and stone size < 5 mm are highly likely to result in successful ESWL.

•

Previous studies have reported a wide variation of ESWL success rate ranging from 46% to 91%.

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Failure of ESWL results in unnecessary exposure of renal parenchyma to shock waves and complications like renal hematoma.

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Increasing Efforts have been made to determine factors that predict ESWL outcome and improve patients' selection.

Artificial intelligence in stone disease

PURPOSE OF REVIEW

Artificial intelligence (AI) is the ability of a machine, or computer, to simulate intelligent behavior. In medicine, the use of large datasets enables a computer to learn how to perform cognitive tasks, thereby facilitating medical decision-making. This review aims to describe advancements in AI in stone disease to improve diagnostic accuracy in determining stone composition, to predict outcomes of surgical procedures or watchful waiting and ultimately to optimize treatment choices for patients.

RECENT FINDINGS

AI algorithms show high accuracy in different realms including stone detection and in the prediction of surgical outcomes. There are machine learning algorithms for outcomes after percutaneous nephrolithotomy, extracorporeal shockwave lithotripsy, and for ureteral stone passage. Some of these algorithms show better predictive capabilities compared to existing scoring systems and nomograms.

SUMMARY

The use of AI can facilitate the development of diagnostic and treatment algorithms in patients with stone disease. Although the generalizability and external validity of these algorithms remain uncertain, the development of highly accurate AI-based tools may enable the urologist to provide more customized patient care and superior outcomes.

Machine learning applications to enhance patient specific care for urologic surgery

PURPOSE

As computational power has improved over the past 20 years, the daily application of machine learning methods has become more prevalent in daily life. Additionally, there is increasing interest in the clinical application of machine learning techniques. We sought to review the current literature regarding machine learning applications for patient-specific urologic surgical care.

METHODS

We performed a broad search of the current literature via the PubMed-Medline and Google Scholar databases up to Dec 2020. The search terms "urologic surgery" as well as "artificial intelligence", "machine learning", "neural network", and "automation" were used.

RESULTS

The focus of machine learning applications for patient counseling is disease-specific. For stone disease, multiple studies focused on the prediction of stone-free rate based on preoperative characteristics of clinical and imaging data. For kidney cancer, many studies focused on advanced imaging analysis to predict renal mass pathology preoperatively. Machine learning applications in prostate cancer could provide for treatment counseling as well as prediction of disease-specific outcomes. Furthermore, for bladder cancer, the reviewed studies focus on staging via imaging, to better counsel patients towards neoadjuvant chemotherapy. Additionally, there have been many efforts on automatically segmenting and matching preoperative imaging with intraoperative anatomy.

CONCLUSION

Machine learning techniques can be implemented to assist patient-centered surgical care and increase patient engagement within their decision-making processes. As data sets improve and expand, especially with the transition to large-scale EHR usage, these tools will improve in efficacy and be utilized more frequently.

Machine Learning Models for Predicting Stone-Free Status after Shockwave Lithotripsy: A Systematic Review and Meta-Analysis

We performed a systematic review and meta-analysis to investigate the use of machine learning techniques for predicting stone-free rates following Shockwave Lithotripsy (SWL). Eight papers (3264 patients) were included. Two studies used decision-tree approaches, five studies utilised Artificial Neural Networks (ANN), and one study combined a variety of approaches. The summary true positive rate was 79%, summary false positive rate was 14%, and Receiver Operator Characteristic (ROC) was 0.90 for machine learning approaches. Machine learning algorithms were at least as good as standard approaches. Further prospective evidence is needed to routinely apply machine learning algorithms in clinical practice.

Artificial intelligence (AI) in urology-Current use and future directions: An iTRUE study

OBJECTIVE

Artificial intelligence (AI) is used in various urological conditions such as urolithiasis, pediatric urology, urogynecology, benign prostate hyperplasia (BPH), renal transplant, and uro-oncology. The various models of AI and its application in urology subspecialties are reviewed and discussed.

MATERIAL AND METHODS

Search strategy was adapted to identify and review the literature pertaining to the application of AI in urology using the keywords "urology," "artificial intelligence," "machine learning," "deep learning," "artificial neural networks," "computer vision," and "natural language processing" were included and categorized. Review articles, editorial comments, and non-urologic studies were excluded.

RESULTS

The article reviewed 47 articles that reported characteristics and implementation of AI in urological cancer. In all cases with benign conditions, artificial intelligence was used to predict outcomes of the surgical procedure. In urolithiasis, it was used to predict stone composition, whereas in pediatric urology and BPH, it was applied to predict the severity of condition. In cases with malignant conditions, it was applied to predict the treatment response, survival, prognosis, and recurrence on the basis of the genomic and biomarker studies. These results were also found to be statistically better than routine approaches. Application of radiomics in classification and nuclear grading of renal masses, cystoscopic diagnosis of bladder cancers, predicting Gleason score, and magnetic resonance imaging with computer-assisted diagnosis for prostate cancers are few applications of AI that have been studied extensively.

CONCLUSIONS

In the near future, we will see a shift in the clinical paradigm as AI applications will find their place in the guidelines and revolutionize the decision-making process.

Precision Medicine, Artificial Intelligence, and Genomic Markers in Urology. Do we need to Tailor our Clinical Practice?

Precision medicine plays a key role in urological oncology practice nowadays, with the breakthrough of the poly (ADP-ribose) polymerase inhibitors (PARPi), which play a critical role in different DNA damage repair (DDR) pathways, the immune checkpoint inhibitors, the genomic expression profiles and current genome manipulation-directed targeted therapy. Information and technology (IT) are set to change the way we assess and treat patients and should be reviewed and discussed. The aim of the present article is to demonstrate a detailed revision on precision medicine, including novel therapeutic targets, genomic markers, genomic stratification of urological patients, and the top-notch technological breakthroughs that could change our clinical practice

We performed a review of the literature in four different databases (PubMed, Embase, Lilacs, and Scielo) on any information concerning prostate, bladder, kidney and urothelial cancer novel treatments with PARPi, immune checkpoint inhibitors (ICIs), targeted therapy with fibroblast growth factor receptor inhibitors (FGFRi), and theranostics with prostate-specific membrane antigen (PSMA) targeted monoclonal antibodies. Artificial intelligence, machine learning, and deep learning algorithm in urological practice were also part of the search. We included all articles written in English, published within the past 7 years, that discussed outstanding therapies and genomics in urological cancer and artificial intelligence applied to urology. Meanwhile, we excluded articles with lack of a clear methodology and written in any other language than English.

One-hundred and twenty-six articles of interest were found; of these, 65 articles that presented novel treatments of urological neoplasms, discussed precision medicine, genomic expression profiles and biomarkers in urology, and latest deep learning and machine learning algorithms as well as the use of artificial intelligence in urological practice were selected. A critical review of the literature is presented in the present article.

Urology is a constantly changing specialty with a wide range of therapeutic breakthroughs, a huge understanding of the genomic expression profiles for each urological cancer and a tendency to use cutting-edge technology to treat our patients. All of these major developments must be analyzed objectively, taking into account costs to the health systems, risks and benefits to the patients, and the legal background that comes with them. A critical analysis of these new technologies and pharmacological breakthroughs should be made before considering changing our clinical practice. Nowadays, research needs to be strengthened to help us improve results in assessing and treating our patients.

The role of extracorporeal shock wave lithotripsy in the future of stone management

PURPOSE OF REVIEW

The most relevant recent findings on the use of extracorporeal shock wave lithotripsy (ESWL) in adult population to provide an insight of its role in the current and future of stone treatment. Comparing ESWL with other modalities is not in the scope of this review.

RECENT FINDINGS

We conducted a PubMed/Embase search and reviewed recent publications that include relevant information on the development of ESWL. Low-rate shock waves improve stone breakage, ramping energy modalities improve stone fragmentation and have lower incidence of hematoma and kidney injury. Transgluteal approach is suggested to improve stone-free rates for distal ureteral stones in a single session. Proper coupling is the most important technical aspect of the treatment and coupling improvement can be achieved by optical monitorization. Triple D score is a promising tool in proper patient selection, but external validation is needed. Predictive information arising from computed tomography scans has been refined by the variant coefficient of stone density and 3D texture analysis that might improve outcomes in the future.

SUMMARY

Recent evidence suggests that modifying techniques and protocols, and better patient selection are the current trends for improving ESWL outcomes. EWSL will keep its role as the single noninvasive treatment in stone management with room for outcome improvement in the future.

Predicting the Postoperative Outcome of Percutaneous Nephrolithotomy with Machine Learning System: Software Validation and Comparative Analysis with Guy's Stone Score and the CROES Nomogram

Purpose: To validate the output of a machine learning-based software as an intelligible interface for predicting multiple outcomes after percutaneous nephrolithotomy (PCNL). We compared the performance of this system with Guy's stone score (GSS) and the Clinical Research Office of Endourological Society (CROES) nomogram. Patients and Methods: Data from 146 adult patients (87 males, 59%) who underwent PCNL at our institute were used. To validate the system, accuracy of the software for predicting each postoperative outcome was compared with the actual outcome. Similarly, preoperative data were analyzed with GSS and CROES nomograms to determine stone-free status as predicted by these nomograms. A receiver operating characteristic (ROC) curve was generated for each scoring system, and the area under the ROC curve (AUC) was calculated and used to assess the predictive performance of all three models. Results: Overall stone-free rate was 72.6% (106/146). Forty of 146 patients (27.4%) were scheduled for 42 ancillary procedures (extracorporeal shockwave lithotripsy [SWL] [n = 31] or repeat PCNL [n = 11]) to manage residual renal stones. Overall, the machine learning system predicted the PCNL outcomes with an accuracy ranging between 80% and 95.1%. For predicting the stone-free status, the AUC for the software (0.915) was significantly larger than the AUC for GSS (0.615) or CROES nomograms (0.621) (p < 0.001). Conclusion: At the internal institutional level, the machine learning-based software was a promising tool for recording, processing, and predicting outcomes after PCNL. Validation of this system against an external dataset is highly recommended before its widespread application.

Artificial intelligence and neural networks in urology: current clinical applications

INTRODUCTION

As we enter the era of "big data," an increasing amount of complex health-care data will become available. These data are often redundant, "noisy," and characterized by wide variability. In order to offer a precise and transversal view of a clinical scenario the artificial intelligence (AI) with machine learning (ML) algorithms and Artificial neuron networks (ANNs) process were adopted, with a promising wide diffusion in the near future. The present work aims to provide a comprehensive and critical overview of the current and potential applications of AI and ANNs in urology.

EVIDENCE ACQUISITION

A non-systematic review of the literature was performed by screening Medline, PubMed, the Cochrane Database, and Embase to detect pertinent studies regarding the application of AI and ANN in Urology.

EVIDENCE SYNTHESIS

The main application of AI in urology is the field of genitourinary cancers. Focusing on prostate cancer, AI was applied for the prediction of prostate biopsy results. For bladder cancer, the prediction of recurrence-free probability and diagnostic evaluation were analysed with ML algorithms. For kidney and testis cancer, anecdotal experiences were reported for staging and prediction of diseases recurrence. More recently, AI has been applied in non-oncological diseases like stones and functional urology.

CONCLUSIONS

AI technologies are growing their role in health care; but, up to now, their "real-life" implementation remains limited. However, in the near future, the potential of AI-driven era could change the clinical practice in Urology, improving overall patient outcomes.

Current and future applications of machine and deep learning in urology: a review of the literature on urolithiasis, renal cell carcinoma, and bladder and prostate cancer

PURPOSE

The purpose of the study was to provide a comprehensive review of recent machine learning (ML) and deep learning (DL) applications in urological practice. Numerous studies have reported their use in the medical care of various urological disorders; however, no critical analysis has been made to date.

METHODS

A detailed search of original articles was performed using the PubMed MEDLINE database to identify recent English literature relevant to ML and DL applications in the fields of urolithiasis, renal cell carcinoma (RCC), bladder cancer (BCa), and prostate cancer (PCa).

RESULTS

In total, 43 articles were included addressing these four subfields. The most common ML and DL application in urolithiasis is in the prediction of endourologic surgical outcomes. The main area of research involving ML and DL in RCC concerns the differentiation between benign and malignant small renal masses, Fuhrman nuclear grade prediction, and gene expression-based molecular signatures. BCa studies employ radiomics and texture feature analysis for the distinction between low- and high-grade tumors, address accurate image-based cytology, and use algorithms to predict treatment response, tumor recurrence, and patient survival. PCa studies aim at developing algorithms for Gleason score prediction, MRI computer-aided diagnosis, and surgical outcomes and biochemical recurrence prediction. Studies consistently found the superiority of these methods over traditional statistical methods.

CONCLUSIONS

The continuous incorporation of clinical data, further ML and DL algorithm retraining, and generalizability of models will augment the prediction accuracy and enhance individualized medicine.

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.

How to compete with Google and Co.: big data and artificial intelligence in stones

PURPOSE OF REVIEW

To elucidate the keywords big data and artificial intelligence and corresponding literature in the field of urolithiasis.

RECENT FINDINGS

Numbers of publications on big data and artificial intelligence in the field of urolithiasis are rising, but still low. Most publications describe the development, testing, and validation of automated computational analyses of clinical data sets and/or images in a preclinical setting.

SUMMARY

In the field of digital health services, there is a discrepancy between the enormous commitment of large private companies and investments of public funds. This situation means a still small number of medical publications on this topic in the urolithiasis field. Nevertheless, as doctors and scientists, we should try to provide our patients with secure and worthwhile digital services.