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

Artificial Intelligence: Ready To Pass the European Board Examinations in Urology?

The role of artificial intelligence (AI) in the medical domain is increasing on an annual basis. AI allows instant access to the latest scientific data in urological surgery, facilitating a level of theoretical knowledge that previously required several years of practice and training. To evaluate the capability of AI to provide robust data in a specialized domain, we submitted the in-service assessment of the European Board of Urology to three different AI tools: ChatGPT 3.5, ChatGPT 4.0, and Bard. The assessment consists of 100 single-answer questions with four multiple-choice options. We compared the responses of 736 participants to the AI responses. The average score for the 736 participants was 67.20. ChatGPT 3.5 scored 59 points, ranking in 570th place. ChatGPT 4.0 scored 80 points, ranking 80th, just on the border of the top 10%. Google Bard scored 68 points, ranking 340th. Our study demonstrates that AI systems have the capability to participate in a urological examination and achieve satisfactory results. However, a critical perspective must be maintained, as current AI systems are not infallible. Finally, the role of AI in the acquisition of knowledge and the dissemination of information remains to be delineated.

Patient summary

We submitted questions from the European Diploma in Urological Surgery to three artificial intelligence (AI) systems. Our findings reveal that AI tools show remarkable performance in assessments of urological surgical knowledge. However, certain limitations were also observed.

Systematic reviews of machine learning in healthcare: a literature review

INTRODUCTION

The increasing availability of data and computing power has made machine learning (ML) a viable approach to faster, more efficient healthcare delivery.

METHODS

A systematic literature review (SLR) of published SLRs evaluating ML applications in healthcare settings published between1 January 2010 and 27 March 2023 was conducted.

RESULTS

In total 220 SLRs covering 10,462 ML algorithms were reviewed. The main application of AI in medicine related to the clinical prediction and disease prognosis in oncology and neurology with the use of imaging data. Accuracy, specificity, and sensitivity were provided in 56%, 28%, and 25% SLRs respectively. Internal and external validation was reported in 53% and less than 1% of the cases respectively. The most common modeling approach was neural networks (2,454 ML algorithms), followed by support vector machine and random forest/decision trees (1,578 and 1,522 ML algorithms, respectively).

EXPERT OPINION

The review indicated considerable reporting gaps in terms of the ML's performance, both internal and external validation. Greater accessibility to healthcare data for developers can ensure the faster adoption of ML algorithms into clinical practice.

Ureteroscopy and lasertripsy with pop dusting using high power holmium laser for large urinary stones > 15 mm: 6.5-year prospective outcomes from a high-volume stone center

INTRODUCTION

Ureteroscopy and stone lasertripsy (URSL) is a recognized technique for treatment of urinary tract stones. Holmium:Yag laser has been successfully used for this purpose for the last two decades. More lately, pulse modulation with Moses technology and high power lasers have been introduced with the result of faster and more efficient stone lasertripsy. Pop dusting is a two-stage combined treatment using a long pulse Ho:YAG laser, initially in contact mode with the stone 'dusting' (0.2-0.5 J/40-50 Hz) followed by non-contact mode 'pop-dusting' (0.5-0.7 J/20-50 Hz). We wanted to look at the outcomes of lasertripsy for renal and ureteric stones using a high-power laser machine.

METHODS

Over a period of 6.5 years (January 2016-May 2022), we prospectively collected data for patients undergoing URSL for stones larger than 15 mm treated using high power Ho:YAG laser (60W Moses or 100W laser). Patient parameters, stone demographics and outcomes of URSL were analyzed.

RESULTS

A total of 201 patients, underwent URSL for large urinary stones. In 136 patients (61.6%) stones were multiple and the mean single and cumulative stone size was 18 mm and 22.4 mm respectively. A pre- and post-operative stent was placed in 92 (41.4%) and 169 (76%) respectively. The initial and final stone free rate (SFR) were 84.5% and 94% respectively and 10% patients underwent additional procedure to achieve stone free status. 7 (3.9%) complications were recorded, all related to UTI/sepsis, with 6 Clavien II and 1 Clavien IVa complication.

CONCLUSION

Dusting and pop-dusting has shown to be successful and safe with the ability to treat large, bilateral or multiple stones with low retreatment and complication rates.

Imaging urolithiasis: complications and interventions in children

Urolithiasis affects people in all age groups, but over the last decades there has been an increasing incidence in children. Typical symptoms include abdominal or flank pain with haematuria; in acute cases dysuria, fever or vomiting also occur. Ultrasound is considered the modality of choice in paediatric urolithiasis because it can be used to identify most clinically relevant stones. Complementary imaging modalities such as conventional radiographs or non-contrast computed tomography should be limited to specific clinical situations. Management of kidney stones includes dietary, pharmacological and urological interventions, depending on stone size, location or type, and the child's condition. With a very high incidence of underlying metabolic abnormalities and significant recurrence rates in paediatric urolithiasis, thorough metabolic evaluation and follow-up examination studies are of utmost importance.

Fifty years of basic and clinical renal stone research: have we achieved major breakthroughs? A debate

PURPOSE OF REVIEW

After 50 years of basic and clinical renal stone research, it is appropriate to evaluate whether breakthroughs have been achieved and if so, how they may be harnessed to combat stone disease therapeutically and prophylactically.

RECENT FINDINGS

Regarding stone therapeutics and prophylaxis, recent innovative studies are sparse. Researchers have resorted to publishing articles derived from data mining. Stone incidence and prevalence have increased during the past 50 years, suggesting the absence of any major breakthroughs. However, new sciences and technologies have created fresh opportunities. Information technology stores huge epidemiological databases leading to identification of new risk factors. Genetic coding has prompted identification of monogenic diseases associated with urolithiasis. Genome-wide association studies in combination with epigenomics, transcriptomics, proteomics, and metabolomics are providing new insights. High-throughput and culture-independent techniques promise to define the impact of microbiome on stone formation while artificial intelligent techniques contribute to diagnosis and prediction of treatment outcomes. These technologies, as well as those which are advancing surgical treatment of stones represent major breakthroughs in stone research.

SUMMARY

Although efforts to cure stones have not yielded major breakthroughs, technological advances have improved surgical management of this disease and represent significant headway in applied stone research.

Reporting quality of abstracts of systematic reviews/meta-analyses: An appraisal of Arab Journal of Urology across 12 years: the PRISMA-Abstracts checklist

Objective

We appraised the reporting quality of abstracts of systematic reviews/meta-analyses (SR/MAs) published in one urology journal and explored associations between abstract characteristics and completeness of reporting.

Methods

The Arab Journal of Urology (AJU) was searched for SR/MAs published between January 2011 and 31 May 2022. SR/MAs with structured abstract and quantitative synthesis were eligible. Two reviewers simultaneously together selected the SR/MAs by title, screened the abstracts, and included those based on inclusion/exclusion criteria. Data of a range of characteristics were extracted from each SR/MAs into a spreadsheet. To gauge completeness of reporting, the PRISMA-Abstract checklist (12 items) was used to appraise the extent to which abstracts adhered to the checklist. For each abstract, we computed item, section, and overall adherence. Chi-square and t-tests compared the adherence scores. Univariate and multivariate analyses identified the abstract characteristics associated with overall adherence.

Results

In total, 66 SR/MAs published during the examined period; 62 were included. Partial reporting was not uncommon. In terms of adherence to the 12 PRISMA-A items were: two items exhibited 100% adherence (title, objectives); five items had 80% to <100% adherence (interpretation, included studies, synthesis of results, eligibility criteria, and information sources); two items displayed 40% to <80% adherence (description of the effect, strengths/limitations of evidence); and three items had adherence that fell between 0% and 1.6% (risk of bias, funding/conflict of interest, registration). Multivariable regression revealed two independent predictors of overall adherence: single-country authorship (i.e. no collaboration) was associated with higher overall adherence (P = 0.046); and abstracts from South America were associated with lower overall adherence (P = 0.04).

Conclusion

This study is the first to appraise abstracts of SR/MAs in urology. For high-quality abstracts, improvements are needed in the quality of reporting. Adoption/better adherence to PRISMA-A checklist by editors/authors could improve the reporting quality and completeness of SR/MAs abstracts.

On the rocks: can urologists identify stone composition based on endoscopic images alone? A worldwide survey of urologists

PURPOSE

As part of the management of nephrolithiasis, determination of chemical composition of stones is important. Our objective in this study is to assess urologists' accuracy in making visual, intraoperative determinations of stone composition.

MATERIALS AND METHODS

We conducted a REDCap survey asking urologists to predict stone composition based on intraoperative images of 10 different pure-composition kidney stones of 7 different types: calcium oxalate monohydrate (COM), calcium oxalate dihydrate (COD), calcium phosphate (CP) apatite, CP brushite, uric acid (UA), struvite (ST) and cystine (CY). To evaluate experience, we examined specific endourologic training, years of experience, and number of ureteroscopy (URS) cases/week. A self-assessment of ability to identify stone composition was also required.

RESULTS

With a response rate of 26% (366 completed surveys out of 1,370 deliveries), the overall accuracy of our cohort was 44%. COM, ST, and COD obtained the most successful identification rates (65.9%, 55.7%, and 52.0%, respectively). The most frequent misidentified stones were CP apatite (10.7%) and CY (14.2%). Predictors of increased overall accuracy included self-perceived ability to determine composition and number of ureteroscopies per week, while years of experience did not show a positive correlation.

CONCLUSIONS

Although endoscopic stone recognition can be an important tool for surgeons, it is not reliable enough to be utilized as a single method for stone identification, suggesting that urologists need to refine their ability to successfully recognize specific stone compositions intraoperatively.

The role of 'artificial intelligence, machine learning, virtual reality, and radiomics' in PCNL: a review of publication trends over the last 30 years

Introduction

We wanted to analyze the trend of publications in a period of 30 years from 1994 to 2023, on the application of 'artificial intelligence (AI), machine learning (ML), virtual reality (VR), and radiomics in percutaneous nephrolithotomy (PCNL)'. We conducted this study by looking at published papers associated with AI and PCNL procedures, including simulation training, with preoperative and intraoperative applications.

Materials and Methods

Although MeSH terms research on the PubMed database, we performed a comprehensive review of the literature from 1994 to 2023 for all published papers on 'AI, ML, VR, and radiomics' in 'PCNL', with papers in all languages included. Papers were divided into three 10-year periods: Period 1 (1994-2003), Period 2 (2004-2013), and Period 3 (2014-2023).

Results

Over a 30-year timeframe, 143 papers have been published on the subject with 116 (81%) published in the last decade, with a relative increase from Period 2 to Period 3 of +427% (p = 0.0027). There was a gradual increase in areas such as automated diagnosis of larger stones, automated intraoperative needle targeting, and VR simulators in surgical planning and training. This increase was most marked in Period 3 with automated targeting with 52 papers (45%), followed by the application of AI, ML, and radiomics in predicting operative outcomes (22%, n = 26) and VR for simulation (18%, n = 21). Papers on technological innovations in PCNL (n = 9), intelligent construction of personalized protocols (n = 6), and automated diagnosis (n = 2) accounted for 15% of publications. A rise in automated targeting for PCNL and PCNL training between Period 2 and Period 3 was +247% (p = 0.0055) and +200% (p = 0.0161), respectively.

Conclusion

An interest in the application of AI in PCNL procedures has increased in the last 30 years, and a steep rise has been witnessed in the last 10 years. As new technologies are developed, their application in devices for training and automated systems for precise renal puncture and outcome prediction seems to play a leading role in modern-day AI-based publication trends on PCNL.

Innovations in Kidney Stone Removal

Urolithiasis is a common clinical condition, and surgical treatment is performed with different minimally invasive procedures, such as ureteroscopy, shockwave lithotripsy and percutaneous nephrolithotomy. Although the transition from open surgery to endourological procedures to treat this condition has been a paradigm shift, ongoing technological advancements have permitted further improvement of clinical outcomes with the development of modern equipment. Such innovations in kidney stone removal are new lasers, modern ureteroscopes, development of applications and training systems utilizing three-dimensional models, artificial intelligence and virtual reality, implementation of robotic systems, sheaths connected to vacuum devices and new types of lithotripters. Innovations in kidney stone removal have led to an exciting new era of endourological options for patients and clinicians alike.

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.

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.