Ex Vivo Renal Stone Characterization with Single-Source Dual-Energy Computed Tomography: A Multiparametric Approach

Artificial intelligence in urolithiasis: a systematic review of utilization and effectiveness

PURPOSE

Mirroring global trends, artificial intelligence advances in medicine, notably urolithiasis. It promises accurate diagnoses, effective treatments, and forecasting epidemiological risks and stone passage. This systematic review aims to identify the types of AI models utilised in urolithiasis studies and evaluate their effectiveness.

METHODS

The study was registered with PROSPERO. Pubmed, EMBASE, Google Scholar, and Cochrane Library databases were searched for relevant literature, using keywords such as 'urology,' 'artificial intelligence,' and 'machine learning.' Only original AI studies on urolithiasis were included, excluding reviews, unrelated studies, and non-English articles. PRISMA guidelines followed.

RESULTS

Out of 4851 studies initially identified, 71 were included for comprehensive analysis in the application of AI in urolithiasis. AI showed notable proficiency in stone composition analysis in 12 studies, achieving an average precision of 88.2% (Range 0.65-1). In the domain of stone detection, the average precision remarkably reached 96.9%. AI's accuracy rate in predicting spontaneous ureteral stone passage averaged 87%, while its performance in treatment modalities such as PCNL and SWL achieved average accuracy rates of 82% and 83%, respectively. These AI models were generally superior to traditional diagnostic and treatment methods.

CONCLUSION

The consolidated data underscores AI's increasing significance in urolithiasis management. Across various dimensions-diagnosis, monitoring, and treatment-AI outperformed conventional methodologies. High precision and accuracy rates indicate that AI is not only effective but also poised for integration into routine clinical practice. Further research is warranted to establish AI's long-term utility and to validate its role as a standard tool in urological care.

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.

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

Objective

To provide a comprehensive review on the existing research and evidence regarding artificial intelligence (AI) applications in the assessment and management of urinary stone disease.

Methods

A comprehensive literature review was performed using PubMed, Scopus, and Google Scholar databases to identify publications about innovative concepts or supporting applications of AI in the improvement of every medical procedure relating to stone disease. The terms ''endourology'', ''artificial intelligence'', ''machine learning'', and ''urolithiasis'' were used for searching eligible reports, while review articles, articles referring to automated procedures without AI application, and editorial comments were excluded from the final set of publications. The search was conducted from January 2000 to September 2023 and included manuscripts in the English language.

Results

A total of 69 studies were identified. The main subjects were related to the detection of urinary stones, the prediction of the outcome of conservative or operative management, the optimization of operative procedures, and the elucidation of the relation of urinary stone chemistry with various factors.

Conclusion

AI represents a useful tool that provides urologists with numerous amenities, which explains the fact that it has gained ground in the pursuit of stone disease management perfection. The effectiveness of diagnosis and therapy can be increased by using it as an alternative or adjunct to the already existing data. However, little is known concerning the potential of this vast field. Electronic patient records, containing big data, offer AI the opportunity to develop and analyze more precise and efficient diagnostic and treatment algorithms. Nevertheless, the existing applications are not generalizable in real-life practice, and high-quality studies are needed to establish the integration of AI in the management of urinary stone disease.

Artificial intelligence in endourology: emerging technology for individualized care

PURPOSE OF REVIEW

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

RECENT FINDINGS

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

SUMMARY

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

Evaluation of split-filter dual-energy CT for characterization of urinary stones

OBJECTIVE

To assess accuracy of dual-energy computed tomography (DECT) to differentiate uric acid from calcium urinary stones in dual-energy split filter vs sequential-spiral vs dual-source acquisition.

METHODS

Thirty-four urinary stones (volume 89.0 ± 77.4 mm³; 17 calcium stones, 17 uric acid stones) were scanned in a water-filled phantom using a split-filter equipped CT scanner (SOMATOM Definition Edge, Siemens Healthineers, Forchheim, Germany) in split-filter mode at 120 kVp and sequential-spiral mode at 80 and 140 kVp. Additional DE scans were acquired at 80 and 140 kVp (tin filter) with a dual-source CT scanner (SOMATOM Definition FLASH, Siemens Healthineers). Scans were performed with a CTDIvol of 7.3 mGy in all protocols. Urinary stone categorization was based on dual energy ratio (DER) using an automated 3D segmentation. As reference standard, infrared spectroscopy was used to determine urinary stone composition.

RESULTS

All three DECT techniques significantly differentiated between uric acid and calcium stones by attenuation values and DERs (p < 0.001 for all). Split-filter DECT provided higher DERs for uric acid stones, when compared with dual-source and sequential-spiral DECT, and lower DERs for calcified stones when compared with dual-source DECT (p < 0.001 for both), leading to a decreased accuracy for material differentiation.

CONCLUSION

Split-filter DECT, sequential-spiral DECT and dual-source DECT all allow for the acquisition of DER to classify urinary stones.

ADVANCES IN KNOWLEDGE

Split-filter DECT enables the differentiation between uric acid and calcium stones despite decreased spectral separation when compared with dual-source and dual-spiral DECT.

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.

Low-dose dual-energy CT for stone characterization: a systematic comparison of two generations of split-filter single-source and dual-source dual-energy CT

PURPOSE

To compare noise texture and accuracy to differentiate uric acid from non-uric acid urinary stones among four different single-source and dual-source DECT approaches in an ex vivo phantom study.

METHODS

Thirty-two urinary stones embedded in gelatin were mounted on a Styrofoam disk and placed into a water-filled phantom. The phantom was imaged using four different DECT approaches: (A) dual-source DECT (DS-DE); (B) 1st generation split-filter single-source DECT (SF1-TB); (C) 2nd generation split-filter single-source DECT (SF2-TB) and (D) 2nd generation split-filter single-source DECT using serial acquisitions (SF2-TS). Two different radiation doses (3 mGy and 6 mGy) were used. Noise texture was compared by assessing the average spatial frequency (f_av) of the normalized noise power spectrum (nNPS). ROC curves for stone classification were computed and the accuracy for different dual-energy ratio cutoffs was derived.

RESULTS

NNPS demonstrated comparable noise texture among A, C, and D (f_av-range 0.18-0.19) but finer noise texture for B (f_av = 0.27). Stone classification showed an accuracy of 96.9%, 96.9%, 93.8%, 93.8% for A, B, C, D for low-dose, respectively, and 100%, 96.9%, 96.9%, 100% for routine dose. The vendor-specified cutoff for the dual-energy ratio was optimal except for the low-dose scan in D for which the accuracy was improved from 93.8 to 100% using an optimized cutoff.

CONCLUSION

Accuracy to differentiate uric acid from non-uric acid stones was high among four single-source and dual-source DECT approaches for low- and routine dose DECT scans. Noise texture differed only slightly for the first-generation split-filter approach.

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.

Efficacy of single-source rapid kV-switching dual-energy CT for characterization of non-uric acid renal stones: a prospective ex vivo study using anthropomorphic phantom

PURPOSE

To investigate the accuracy of rapid kV-switching single-source dual-energy computed tomography (rsDECT) for prediction of classes of non-uric-acid stones.

MATERIALS AND METHODS

Non-uric-acid renal stones retrieved via percutaneous nephrolithotomy were prospectively collected between January 2017 and February 2018 in a single institution. Only stones ≥ 5 mm and with pure composition (i.e., ≥ 80% composed of one component) were included. Stone composition was determined using Fourier Transform Infrared Spectroscopy. The stones were scanned in 32-cm-wide anthropomorphic whole-body phantom using rsDECT. The effective atomic number (Zeff), the attenuation at 40 keV (HU40), 70 keV (HU70), and 140 keV (HU140) virtual monochromatic sets of images as well as the ratios between the attenuations were calculated. Values of stone classes were compared using ANOVA and Mann-Whitney U test. Receiver operating curves and area under curve (AUC) were calculated. A p value < 0.05 was considered statistically significant.

RESULTS

The final study sample included 31 stones from 31 patients consisting of 25 (81%) calcium-based, 4 (13%) cystine, and 2 (6%) struvite pure stones. The mean size of the stones was 9.9 ± 2.4 mm. The mean Zeff of the stones was 12.01 ± 0.54 for calcium-based, 11.10 ± 0.68 for struvite, and 10.23 ± 0.75 for cystine stones (p < 0.001). Zeff had the best efficacy to separate different classes of stones. The calculated AUC was 0.947 for Zeff; 0.833 for HU40; 0.880 for HU70; and 0.893 for HU140.

CONCLUSION

Zeff derived from rsDECT has superior performance to HU and attenuation ratios for separation of different classes of non-uric-acid stones.

The clinical role of X-ray computed tomography to predict the clinical efficiency of extracorporeal shock wave lithotripsy

AIM

To evaluate the clinical efficiency of computed tomography for diagnostics of patients with urolithiasis and the choice of treatment strategy.

MATERIAL AND METHODS

The study was carried out at the Urological Clinic of I.M. Sechenov First Moscow State Medical University and included 1044 patients with urinary stones. The ultimate goal of this study was to predict the clinical efficiency of extracorporeal shock wave lithotripsy using a combination of computed tomography and densitometry. Extracorporeal shock wave lithotripsy was performed on "Siemens Lithostar Plus," "Siemens Modularis Uro," and "Dornier Gemini" lithotripters. Statistical analysis of clinical data included evaluation of individual sampling groups and calculation of weighted arithmetic mean ( M).

RESULTS

The efficiency of extracorporeal shock wave lithotripsy has been determined primarily using X-ray analysis of the concrement outlines and the structure (homogeneous or heterogeneous) of its central zone. However, in terms of efficiency and repetition rate (the number of fragmentation procedures required for complete clearance) of extracorporeal shock wave lithotripsy, the mean density of the concrement along the whole length of its three-dimensional structure (expressed in Hounsfield units) appeared to be the most reliable and informative predictive index in this study.

CONCLUSION

The combination of computed tomography with densitometry in the treatment of patients with urolithiasis allows one (1) to determine the exact localization, size, X-ray structure, and structural density of urinary stones and (2) to predict, on the basis of densitometric data histograms, the clinical efficiency and repetition rate of extracorporeal shock wave lithotripsy with due regard to the X-ray structure of peripheral and central zones, and mean density (in Hounsfield units) of urinary concrements.

Rapid kV-switching single-source dual-energy CT ex vivo renal calculi characterization using a multiparametric approach: refining parameters on an expanded dataset

PURPOSE

We aimed to determine the best algorithms for renal stone composition characterization using rapid kV-switching single-source dual-energy computed tomography (rsDECT) and a multiparametric approach after dataset expansion and refinement of variables.

METHODS

rsDECT scans (80 and 140 kVp) were performed on 38 ex vivo 5- to 10-mm renal stones composed of uric acid (UA; n = 21), struvite (STR; n = 5), cystine (CYS; n = 5), and calcium oxalate monohydrate (COM; n = 7). Measurements were obtained for 17 variables: mean Hounsfield units (HU) at 11 monochromatic keV levels, effective Z, 2 iodine-water material basis pairs, and 3 mean monochromatic keV ratios (40/140, 70/120, 70/140). Analysis included using 5 multiparametric algorithms: Support Vector Machine, RandomTree, Artificial Neural Network, Naïve Bayes Tree, and Decision Tree (C4.5).

RESULTS

Separating UA from non-UA stones was 100% accurate using multiple methods. For non-UA stones, using a 70-keV mean cutoff value of 694 HU had 100% accuracy for distinguishing COM from non-COM (CYS, STR) stones. The best result for distinguishing all 3 non-UA subtypes was obtained using RandomTree (15/17, 88%).

CONCLUSIONS

For stones 5 mm or larger, multiple methods can distinguish UA from non-UA and COM from non-COM stones with 100% accuracy. Thus, the choice for analysis is per the user's preference. The best model for separating all three non-UA subtypes was 88% accurate, although with considerable individual overlap between CYS and STR stones. Larger, more diverse datasets, including in vivo data and technical improvements in material separation, may offer more guidance in distinguishing non-UA stone subtypes in the clinical setting.

Dual energy CT - a possible new method to assess regression of rectal cancers after neoadjuvant treatment

BACKGROUND AND OBJECTIVES

The measurement of tumor regression after neoadjuvant oncological treatment has gained increasing interest because it has a prognostic value and because it may influence the method of treatment in rectal cancer. The assessment of tumor regression remains difficult and inaccurate with existing methods. Dual Energy Computed Tomography (DECT) enables qualitative tissue differentiation by simultaneous scanning with different levels of energy. We aimed to assess the feasibility of DECT in quantifying tumor response to neoadjuvant therapy in loco-advanced rectal cancer.

METHODS

We enrolled 11 patients with histological and MRI verified loco-advanced rectal adenocarcinoma and followed up on them prospectively. All patients had one DECT scanning before neoadjuvant treatment and one 12 weeks after using the spectral imaging scan mode. DECT analyzing tools were used to determine the average quantitative parameters; effective-Z, water- and iodine-concentration, Dual Energy Index (DEI), and Dual Energy Ratio (DER). These parameters were compared to the regression in the resection specimen as measured by the pathologist.

RESULTS

Changes in the quantitative parameters differed significantly after treatment in comparison with pre-treatment, and the results were different in patients with different CRT response rates.

CONCLUSION

DECT might be helpful in the assessment of rectal cancer regression grade after neoadjuvant treatment.