Use of a neural network to predict stone growth after shock wave lithotripsy

Bioinformatics in urology - molecular characterization of pathophysiology and response to treatment

The application of bioinformatics has revolutionized the practice of medicine in the past 20 years. From early studies that uncovered subtypes of cancer to broad efforts spearheaded by the Cancer Genome Atlas initiative, the use of bioinformatics strategies to analyse high-dimensional data has provided unprecedented insights into the molecular basis of disease. In addition to the identification of disease subtypes - which enables risk stratification - informatics analysis has facilitated the identification of novel risk factors and drivers of disease, biomarkers of progression and treatment response, as well as possibilities for drug repurposing or repositioning; moreover, bioinformatics has guided research towards precision and personalized medicine. Implementation of specific computational approaches such as artificial intelligence, machine learning and molecular subtyping has yet to become widespread in urology clinical practice for reasons of cost, disruption of clinical workflow and need for prospective validation of informatics approaches in independent patient cohorts. Solving these challenges might accelerate routine integration of bioinformatics into clinical settings.

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.

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.

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.

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.

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 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.

Nonlinear logistic regression model for outcomes after endourologic procedures: a novel predictor

The purpose of this study was to design a thorough and practical nonlinear logistic regression model that can be used for outcome prediction after various forms of endourologic intervention. Input variables and outcome data from 382 renal units endourologically treated at a single institution were used to build and cross-validate an independently designed nonlinear logistic regression model. Model outcomes were stone-free status and need for a secondary procedure. The model predicted stone-free status with sensitivity 75.3% and specificity 60.4%, yielding a positive predictive value (PPV) of 75.3% and negative predictive value (NPV) of 60.4%, with classification accuracy of 69.6%. Receiver operating characteristic area under the curve (ROC AUC) was 0.749. The model predicted the need for a secondary procedure with sensitivity 30% and specificity 98.3%, yielding a PPV of 60% and NPV of 94.2%. ROC AUC was 0.863. The model had equivalent predictive value to a traditional logistic regression model for the secondary procedure outcome. This study is proof-of-concept that a nonlinear regression model adequately predicts key clinical outcomes after shockwave lithotripsy, ureteroscopic lithotripsy, and percutaneous nephrolithotomy. This model holds promise for further optimization via dataset expansion, preferably with multi-institutional data, and could be developed into a predictive nomogram in the future.

Predicting hypogonadism in men based upon age, presence of erectile dysfunction, and depression

Hypogonadism, a disorder associated with aging, can cause significant morbidity. As clinical manifestations of hypogonadism can be subtle, the challenge and the burden of diagnosis remain the responsibility of the clinician. Four different analytic methods were used to predict hypogonadism in men based upon age, the presence of erectile dysfunction (ED) and depression. 218 men were classified by age, serum testosterone level, the presence of ED and depression. Depression was determined by the Center for Epidemiologic Studies Depression Scale (CES-D). ED was assessed by the Sexual Health Inventory for Men (SHIM). Hypogonadism was defined as a serum testosterone level <300 ng/dl. An artificial neural network (ANN) was programmed and trained to predict hypogonadism based upon age, SHIM, and CES-D scores. Subject data was randomly partitioned into a training set of 148 (67.9%) and a test set of 70 (32.1%). The ANN processed the test set only after the training was complete. The discrete predicted binary output was set to (0) if testosterone level was <300 ng/dl or (1) if >300 ng/dl. The data was also analyzed by standard logistic regression (LR), linear and quadratic discriminant function analysis (LDFA and QDFA, respectively). Reverse regression (RR) analysis evaluated the statistical significance of each risk factor. The ANN can accurately predict hypogonadism in men based upon age, the presence of ED, and depression (receiver-operating characteristic=0.725). A four hidden node network was found to have the highest accuracy. RR revealed the depression index score to be most significant variable (P=0.0019), followed by SHIM score (P=0.00602), and then by age (P=0.015). Hypogonadism can be predicated by an ANN using the input factors of age, ED, and depression. This model can help clinicians assess the need for endocrinologic evaluation in men.

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.

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

OBJECTIVE

To assess the ability of artificial neural networks (ANNs) to predict optimum renal stone fragmentation in patients being managed by extracorporeal shock wave lithotripsy (ESWL).

PATIENTS AND METHODS

The study included 82 patients with renal stones who were being treated by ESWL. Data (input and output values) from 60 patients in whom there was optimum fragmentation of stones by ESWL were used for training the ANN. These data mainly included the 24-h urinary variables, the radiological features of the stone disease and the ESWL settings used. The predictability of the trained ANN was tested on 22 subsequent patients, by supplying the input variables of the 22 patients into the trained ANN and recording the output values (predicted values). After subjecting these patients to ESWL, the actual results (observed values) were recorded. The predicted and the observed values were then compared.

RESULTS

In the 22 patients in whom predictability was tested, the trained ANN predicted optimum fragmentation at < or = 13 000 shocks/stone (as per study protocol) in 17 and optimum fragmentation at> 13 000 shocks/stone in the other five. In the 17 patients (test set) where the trained ANN had predicted optimum fragmentation at < or = 13 000 shocks/stone, the optimum fragmentation was at that value, although the predicted and observed values were not identical. The overall correlation between the predicted and the observed values was 75.5% (correlation coefficient 0.7547) in these 17 patients. Of the other five patients, none had optimum fragmentation at < 13 000 shocks/stone, as predicted by the trained ANN, giving complete accuracy for this factor.

CONCLUSION

This was a pilot study, i.e. an initial attempt to use an ANN in this regard, and although there were few patients, such that it is not possible to make final recommendations, the overall predictability was approximately 75%. An encouraging outcome of the study was that the trained ANN identified patients unlikely to benefit from ESWL. Using a larger dataset and identifying more significant variables, while eliminating inputs with a negative effect, the efficiency and utility of this ANN can probably be enhanced and in future it might be possible to predict stone fragmentation with reasonable accuracy.

A review of evidence of health benefit from artificial neural networks in medical intervention

The purpose of this review is to assess the evidence of healthcare benefits involving the application of artificial neural networks to the clinical functions of diagnosis, prognosis and survival analysis, in the medical domains of oncology, critical care and cardiovascular medicine. The primary source of publications is PUBMED listings under Randomised Controlled Trials and Clinical Trials. The rĵle of neural networks is introduced within the context of advances in medical decision support arising from parallel developments in statistics and artificial intelligence. This is followed by a survey of published Randomised Controlled Trials and Clinical Trials, leading to recommendations for good practice in the design and evaluation of neural networks for use in medical intervention.

Management of the extracorporeal shock wave lithotripsy patient

Over 2 million Americans experience kidney and urinary stone disease each year. Early treatments resulted in high mortality and morbidity rates. With the advent of extracorporeal shock wave lithotripsy less than 20 years ago, treatment for this disease has become far safer with more rapid recovery and fewer complications. The selection of patients eligible for extracorporeal shock wave lithotripsy is dependent on the location and size of the stones and the overall health of the patient. This article discusses the different treatment modalities used for stone disease and the different methods currently available for extracorporeal shock wave lithotripsy. Preprocedure preparation of the patient and postoperative care for this population is reviewed in detail.

Current advances in shock wave lithotripsy

The introduction of shock wave lithotripsy into clinical practice revolutionized the management of urinary tract stone disease. As experience has been gained with its use, however, the limitations and adverse effects associated with it have been recognized.

Renal calculi

This past year produced some remarkable reports on renal (and other urinary) calculi. Randall's plaques have returned, phosphate relates to insulin and lipid metabolism, and sialic acid is out. Risk factors for astronauts, cystinuric individuals, older and indinavir patients achieve importance. Discovery by spiral computed tomography advances, teleconsultation emerges and shot-gun therapy with potassium-magnesium citrate succeeds. Endoscopic or shock wave lithotripsy vie for which is best, and both attempt to eliminate open surgery. Yet open surgery still has its place.

The Use of Extracorporeal Shock Wave Lithotripsy (Eswl) in Solitary Kidney Our Experience

Additional endourologic procedures before ESWL remain controversial for patients with lithiasis in solitary kidneys. The authors report their experience on 31 patients treated with a Dornier MFL 5000 lithotriptor. They conclude that if there is no ureteral obstruction prior to ESWL, the majority of patients with stone size smaller than 20 mm do not need pre or post operation manipulation.