Development of a novel magnetic resonance imaging acquisition and analysis workflow for the quantification of shock wave lithotripsy-induced renal hemorrhagic injury

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.

Functional and Morphological Changes Associated with Burst Wave Lithotripsy-Treated Pig Kidneys

Purpose: Burst wave lithotripsy (BWL) is a new technique for comminution of urinary stones. This technology is noninvasive, has a low positive pressure magnitude, and is thought to produce minor amounts of renal injury. However, little is known about the functional changes related to BWL treatment. In this study, we sought to determine if clinical BWL exposure produces a functional or morphological change in the kidney. Materials and Methods: Twelve female pigs were prepared for renal clearance assessment and served as either sham time controls (6) or were treated with BWL (6). In the treated group, 1 kidney in each pig was exposed to 18,000 pulses at 10 pulses/s with 20 cycles/pulse. Pressure levels related to each pulse were 12 and -7 MPa. Inulin (glomerular filtration rate, GFR) and para-aminohippuric acid (effective renal plasma flow, eRPF) clearance was measured before and 1 hour after treatment. Lesion size analysis was performed to assess the volume of hemorrhagic tissue injury created by each treatment (% FRV). Results: No visible gross hematuria was observed in any of the collected urine samples of the treated kidneys. BWL exposure also did not lead to a change in GFR or eRPF after treatment, nor did it cause a measurable amount of hemorrhage in the tissue. Conclusion: Using the clinical treatment parameters employed in this study, BWL did not cause an acute change in renal function or a hemorrhagic lesion.

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.

Renal Protection Phenomenon Observed in a Porcine Model After Electromagnetic Lithotripsy Using a Treatment Pause

Purpose: Pretreating the kidney with 100 low-energy shock waves (SWs) with a time pause before delivering a clinical dose of SWs (Dornier HM-3, 2000 SWs, 24 kV, and 120 SWs/min) has been shown to significantly reduce the size of the hemorrhagic lesion produced in that treated kidney, compared to a protocol without pretreatment. It has been assumed that a similar reduction in injury will occur with lithotripters other than the HM-3, but experiments to confirm this assumption are lacking. In this study, we sought to verify that the lesion protection phenomenon also occurs in a lithotripter using an electromagnetic shock source and dry-head coupling. Materials and Methods: Eleven female pigs were placed in a Dornier Compact S lithotripter where the left kidney of each animal was targeted for lithotripsy treatment. Some kidneys received 2500 SWs at power level (PL) = 5 (120 SWs/min) while some kidneys were pretreated with 100 SWs at PL = 1, with a 3-minute time pause, followed immediately by 2500 SWs at PL = 5 (120 SWs/min). Lesion size analysis was performed to assess the volume of hemorrhagic tissue injury created by each treatment regimen (% functional renal volume). Results: Lesion size fell by 85% (p = 0.01) in the 100 SW pretreatment group compared to the injury produced by a regimen without pretreatment. Conclusions: The results suggest that the treatment pause protection phenomenon occurs with a Dornier Compact S, a machine distinctly different from the Dornier HM-3. This result also suggests that this phenomenon may be observed generally in SW lithotripters.

Evaluation of Renal Stone Comminution and Injury by Burst Wave Lithotripsy in a Pig Model

Introduction: Burst wave lithotripsy is an experimental technology to noninvasively fragment kidney stones with focused bursts of ultrasound (US). This study evaluated the safety and effectiveness of specific lithotripsy parameters in a porcine model of nephrolithiasis. Methods: A 6- to 7-mm human kidney stone was surgically implanted in each kidney of three pigs. A burst wave lithotripsy US transducer with an inline US imager was coupled to the flank and the lithotripter focus was aligned with the stone. Each stone was exposed to burst wave lithotripsy at 6.5 to 7 MPa focal pressure for 30 minutes under real-time image guidance. After treatment, the kidneys were removed for gross, histologic, and MRI assessment. Stone fragments were retrieved from the kidney to determine the mass comminuted to pieces <2 mm. Results: On average, 87% of the stone mass was reduced to fragments <2 mm. In three of five treatments, stones were completely comminuted to <2-mm fragments. In two of five treatments, stones were partially disintegrated, but larger fragments remained. One stone was not treated because no suitable acoustic window was identified. No injury was detected through gross, histologic, or MRI examination in the parenchymal tissue, although petechial damage and surface erosion were identified on the urothelium of the collecting system limited to the area around the stone. Conclusion: Burst wave lithotripsy can consistently produce stone fragments small enough to spontaneously pass by transcutaneous administration of US pulses. The data suggest that such exposures produce minimal injury to the kidney and urinary tract.

Detection and Evaluation of Renal Injury in Burst Wave Lithotripsy Using Ultrasound and Magnetic Resonance Imaging

PURPOSE

Burst wave lithotripsy (BWL) is a transcutaneous technique with potential to safely and effectively fragment renal stones. Preclinical investigations of BWL require the assessment of potential renal injury. This study evaluates the capabilities of real-time ultrasound and MRI to detect and evaluate BWL injury that was induced in porcine kidneys.

MATERIALS AND METHODS

Ten kidneys from five female farm pigs were treated with either a 170 or 335 kHz BWL transducer using variable treatment parameters and monitored in real-time with ultrasound. Eight kidneys were perfusion fixed and scanned with a 3-Tesla MRI scanner (T1-weighted, T2-weighted, and susceptibility-weighted imaging), followed by processing via an established histomorphometric technique for injury quantification. In addition, two kidneys were separately evaluated for histologic characterization of injury quality.

RESULTS

Observed B-mode hyperechoes on ultrasound consistent with cavitation predicted the presence of BWL-induced renal injury with a sensitivity and specificity of 100% in comparison to the histomorphometric technique. Similarly, MRI detected renal injury with a sensitivity of 90% and specificity of 100% and was able to identify the scale of lesion volumes. The injuries purposefully generated with BWL were histologically similar to those formed by shock wave lithotripsy.

CONCLUSIONS

BWL-induced renal injury can be detected with a high degree of sensitivity and specificity by real-time ultrasound and post-treatment ex vivo MRI. No injury occurred in this study without cavitation detected on ultrasound. Such capabilities for injury detection and lesion volume quantification on MRI can be used for preclinical testing of BWL.