Submillisievert Abdominal Photon-Counting CT versus Energy-integrating Detector CT for Urinary Calculi Detection: Impact on Diagnostic Confidence.

Background Contrast-unenhanced abdominal CT is the imaging standard for urinary calculi detection; however, studies comparing photon-counting detector (PCD) CT and energy-integrating detector (EID) CT dose-reduction potentials are lacking. Purpose To compare the radiation dose and image quality of optimized EID CT with those of an experimental PCD CT scan protocol including tin prefiltration in patients suspected of having urinary calculi. Materials and Methods This retrospective single-center study included patients who underwent unenhanced abdominal PCD CT or EID CT for suspected urinary caliculi between February 2022 and March 2023. Signal and noise measurements were performed at three anatomic levels (kidney, psoas, and obturator muscle). Nephrolithiasis and/or urolithiasis presence was independently assessed by three radiologists, and diagnostic confidence was recorded on a five-point scale (1, little to no confidence; 5, complete confidence). Reader agreement was determined by calculating Krippendorff α. Results A total of 507 patients (mean age, 51.7 years ± 17.4 [SD]; 317 male patients) were included (PCD CT group, 229 patients; EID CT group, 278 patients). Readers 1, 2, and 3 detected nephrolithiasis in 129, 127, and 129 patients and 94, 94, and 94 patients, whereas the readers detected urolithiasis in 113, 114, and 114 patients and 152, 153, and 152 patients in the PCD CT and EID CT groups, respectively. Regardless of protocol (PCD CT or EID CT) or calculus localization, near perfect interreader agreement was found (α ≥ 0.99; 95% CI: 0.99, 1). There was no evidence of a difference in reader confidence between PCD CT and EID CT (median confidence, 5; IQR, 5-5; P ≥ .57). The effective doses were 0.79 mSv (IQR, 0.63-0.99 mSv) and 1.39 mSv (IQR, 1.01-1.87 mSv) for PCD CT and EID CT, respectively. Despite the lower radiation exposure, the signal-to-noise ratios at the kidney, psoas, and obturator levels were 30%, 23%, and 17% higher, respectively, in the PCD CT group (P < .001). Conclusion Submillisievert abdominal PCD CT provided high-quality images for the diagnosis of urinary calculi; radiation exposure was reduced by 44% with a higher signal-to-noise ratio than with EID CT and with no evidence of a difference in reader confidence. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Nezami and Malayeri in this issue.

Accurate prediction of pure uric acid urinary stones in clinical context via a combination of radiomics and machine learning.

PURPOSE: Oral chemolysis is an effective and non-invasive treatment for uric acid urinary stones. This study aimed to classify urinary stones into either pure uric acid (pUA) or other composition (Others) using non-contrast-enhanced computed tomography scans (NCCTs). METHODS: Instances managed at our institution from 2019 to 2021 were screened. They were labeled as either pUA or Others based upon composition analyses, and randomly split into training or testing data set. Several instances contained multiple NCCTs which were all collected. In each of NCCTs, individual urinary stone was treated as individual sample. From manually drawn volumes of interest, we extracted original and wavelet radiomics features for each sample. The most important features were then selected via the Least Absolute Shrinkage and Selection Operator for building the final model on a Support Vector Machine. Performance on the testing set was evaluated via accuracy, sensitivity, specificity, and area under the precision-recall curve (AUPRC). RESULTS: There were 302 instances, of which 118 had pUA urinary stones, generating 576 samples in total. From 851 original and wavelet radiomics features extracted for each sample, 10 most important features were ultimately selected. On the testing data set, accuracy, sensitivity, specificity, and AUPRC were 93.9%, 97.9%, 92.2%, and 0.958, respectively, for per-sample prediction, and 90.8%, 100%, 87.5%, and 0.902, respectively, for per-instance prediction. CONCLUSION: The machine learning algorithm trained with radiomics features from NCCTs can accurately predict pUA urinary stones. Our work suggests a potential assisting tool for stone disease treatment selection.

The most common types of uroliths larger than 1 mm are readily visible and accurately measured in an in vitro setting mimicking the canine abdomen using digital radiography.

Abdominal radiography is an important diagnostic to detect uroliths. Cystine and urate uroliths were historically characterized as nonmineral opaque on survey radiographs. However, recent research and clinical observations indicate that pure urate and cystine uroliths may be detected with digital radiography. The primary purpose of this prospective, in vitro, diagnostic accuracy study was to determine the sensitivity of digital radiography in detecting cystoliths of varying size and composition. Forty canine uroliths of pure composition (10 each of calcium oxalate, struvite, cystine, and urate), acquired from Minnesota Urolith Center and ranging from 1 to 10 mm, were placed in phantoms of three various sizes and radiographed. The radiographs, including three sets of each urolith separately, were evaluated by three blinded radiologists on two separate occasions. Evaluation included presence or absence of urolith, number of uroliths, and maximum diameter of the urolith(s). For all four types of uroliths and all readers, the specificity and PPV were 100% with an associated very high sensitivity (94.4%-98.9%) and NPV (94.8%-98.9%). Calcium oxalate uroliths were the most accurately measured and struvite were the least accurately measured when compared with the gross measurement. Smaller uroliths were more accurately measured than larger uroliths. Uroliths placed in smaller bladder phantoms were more accurately measured than in larger bladder phantoms. Though accurate measurement of uroliths is complicated by and dependent on numerous variables, our results reveal that urate and cystine uroliths are visualized on digital radiography making them a relevant differential diagnosis when seen clinically.

Use of dual energy CT urography in evaluation of urinary stone and complex cyst.

BACKGROUND: Dual-energy computed tomography scans can provide significant benefits to the urinary system. The aim of this study is to determine the limitations and benefits of using dual energy CT urography in patients with urinary system stones and cysts. METHODS: In the analysis of the images, the virtual noncontrasted images obtained from the combined nephrogenicexcretory phase and the true noncontrasted images were evaluated. The true noncontrast images were accepted as the gold standard for stone detection. RESULTS: Eighty-three different stones were detected in 26 of the 115 patients included in the study. Sensibilities of virtual noncontrast images in detecting urinary system stones were 66.7% and 65.4% according to the first and second radiologists, respectively. In this study, 32 hyperdense cysts were detected. According to iodine map images, there was no enhancement in 26 of 32 cysts; only 5 cysts showed minimal contrast enhancement. One patient could not decide on contrast enhancement. DISCUSSION: As a result, if CT urography is performed with dual energy, it can provide additional information in patients with urinary system disorder.

Towards automatic recognition of pure and mixed stones using intra-operative endoscopic digital images.

OBJECTIVE: To assess automatic computer-aided in situ recognition of the morphological features of pure and mixed urinary stones using intra-operative digital endoscopic images acquired in a clinical setting. MATERIALS AND METHODS: In this single-centre study, a urologist with 20 years' experience intra-operatively and prospectively examined the surface and section of all kidney stones encountered. Calcium oxalate monohydrate (COM) or Ia, calcium oxalate dihydrate (COD) or IIb, and uric acid (UA) or IIIb morphological criteria were collected and classified to generate annotated datasets. A deep convolutional neural network (CNN) was trained to predict the composition of both pure and mixed stones. To explain the predictions of the deep neural network model, coarse localization heat-maps were plotted to pinpoint key areas identified by the network. RESULTS: This study included 347 and 236 observations of stone surface and stone section, respectively; approximately 80% of all stones exhibited only one morphological type and approximately 20% displayed two. A highest sensitivity of 98% was obtained for the type 'pure IIIb/UA' using surface images. The most frequently encountered morphology was that of the type 'pure Ia/COM'; it was correctly predicted in 91% and 94% of cases using surface and section images, respectively. Of the mixed type 'Ia/COM + IIb/COD', Ia/COM was predicted in 84% of cases using surface images, IIb/COD in 70% of cases, and both in 65% of cases. With regard to mixed Ia/COM + IIIb/UA stones, Ia/COM was predicted in 91% of cases using section images, IIIb/UA in 69% of cases, and both in 74% of cases. CONCLUSIONS: This preliminary study demonstrates that deep CNNs are a promising method by which to identify kidney stone composition from endoscopic images acquired intra-operatively. Both pure and mixed stone composition could be discriminated. Collected in a clinical setting, surface and section images analysed by a deep CNN provide valuable information about stone morphology for computer-aided diagnosis.

Sensitivity of virtual non-contrast dual-energy CT urogram for detection of urinary calculi: a systematic review and meta-analysis.

OBJECTIVE: To determine the sensitivity of dual-energy (DE) virtual non-contrast computed tomography (vNCT), generated from the excretory phase of a CT urogram, compared to true non-contrast CT (tNCT) for the detection of urinary calculi. METHODS: A search of multiple medical literature databases was performed using predetermined search terms. Inclusion and exclusion criteria were applied, and bias risk was assessed by two independent reviewers using the quality assessment of diagnostic accuracy studies (QUADAS) tool. Collated estimates of sensitivity were generated, and sources of heterogeneity were identified and reviewed. RESULTS: Thirteen studies (1760 patients; 1740 urinary calculi) were included for sensitivity assessment. Pooled sensitivity for urinary calculi on vNCT was 78.1% (95% CI: 70.2 to 85.0%); however, heterogeneity between studies was very high (I2 = 92.0%). Sources of heterogeneity between studies were explored through subgroup analysis by categorising studies according to slice thickness (≥ 2 mm and < 2 mm), use of oral hydration, and use of intravenous furosemide. Pooled sensitivity for detection of urinary calculi on vNCT for studies that used oral hydration and < 2 mm slice thickness was 92.2% (95% CI: 89.5 to 94.5%). Pooled specificity was not performed as true negatives were not reported in most studies. Potential sources of bias were identified in included studies. CONCLUSION: vNCT demonstrated a moderate pooled sensitivity compared to tNCT for the detection of urinary calculi in split bolus CT urogram protocols. However, subgroup analysis suggests higher sensitivity when employing oral hydration and < 2 mm slice thickness or increment. KEY POINTS: • vNCT demonstrated moderate pooled sensitivity for the detection of urinary calculi in split bolus CT urogram protocols. • Subgroup analysis suggested higher sensitivity with oral hydration and < 2 mm slice thickness or increment.

Virtual Unenhanced Dual-Energy CT Images Obtained with a Multimaterial Decomposition Algorithm: Diagnostic Value for Renal Mass and Urinary Stone Evaluation.

Background Virtual unenhanced (VUE) images obtained by using a dual-energy CT (DECT) multimaterial decomposition algorithm hold promise for diagnostic use in the abdomen in lieu of true unenhanced (TUE) images. Purpose To assess VUE images obtained from a DECT multimaterial decomposition algorithm in patients undergoing renal mass and urinary stone evaluation. Materials and Methods In this retrospective Health Insurance Portability and Accountability Act-compliant study, DECT was performed in patients undergoing evaluation for renal mass or urinary stone. VUE images were compared quantitatively to TUE images and qualitatively assessed by four independent radiologists. Differences in attenuation between VUE and TUE images were summarized by using 95% limits of agreement. Diagnostic performance in urinary stone detection was summarized by using area under the receiver operating characteristic curve, sensitivity, and specificity. Results A total of 221 patients (mean age ± standard deviation, 61 years ± 14; 129 men) with 273 renal masses were evaluated. Differences in renal mass attenuation between VUE and TUE images were within 3 HU for both enhancing masses (95% limits of agreement, -3.1 HU to 2.7 HU) and nonenhancing cysts (95% limits of agreement, -2.9 HU to 2.5 HU). Renal mass classification as enhancing mass versus nonenhancing cyst did not change (reclassification rate of enhancing masses, 0% [0 of 78]; 95% CI: 0, 5; reclassification rate of nonenhancing cysts, 0% [0 of 193]; 95% CI: 0, 2) with use of VUE in lieu of TUE images. Among 166 urinary stones evaluated, diagnostic performance of VUE images for stone detection was lower compared with that of TUE images (area under the receiver operating characteristic curve, 0.79 [95% CI: 0.73, 0.84] vs 0.93 [95% CI: 0.91, 0.95]; P < .001) due to reduced sensitivity of VUE for detection of stones 3 mm in diameter or less compared with those greater than 3 mm (sensitivity, 23% [25 of 108; 95% CI: 12, 40] vs 88% [126 of 144; 95% CI: 77, 94]; P < .001). Conclusion Compared with true unenhanced images, virtual unenhanced (VUE) images were unlikely to change renal mass classification as enhancing mass versus nonenhancing cyst. Diagnostic performance of VUE images remained suboptimal for urinary stone detection due to subtraction of stones 3 mm or less in diameter. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Sosna in this issue.

Very low-dose computerized tomography for confirmation of urinary stone presence.

PURPOSE: To determine whether a modified non-contrast very low-dose computed tomography (VLD-CT) protocol is applicable for confirmation of known urolithiasis. METHODS AND MATERIALS: Consecutive adult patients with a CT scan showing urinary tract stone(s) between 6/2017-12/2018 were included. They were referred to a modified VLD-CT protocol if stone presence was equivocal or if stone location needed reassessment before an endourological interventional procedure. The scanned area was limited to the level of initial stone location caudally. Data on patients' demographics andbody mass index, were collected. The scanned length and radiation dose were calculated. Images were reviewed by two radiologists who assessed stone size and location. Follow-up reference standard included stone passage, surgical removal, and other imaging and clinical information. RESULTS: Sixty-three patients [63 stones, mean BMI 28.7 (range 19-41.9)] were included. VLD-CTs revealed 31 stones in 31 patients, with a mean stone length of 5.5 mm. Fifteen stones remained at the same location, and 16 had migrated, of which two appeared in the bladder. Thirty-two stones were not observed on VLD-CT. The mean span scanned on the VLD-CT was 274 mm (± 80). The average radiation exposure was 1.47 mGy (range 1.09-3.3), and the absorbed dose was 0.77 mSv (range 0.39-1.43), compared to 10.24 mGy (range 1.75-28.9) and 7.87 mSv (range 1.44-18.5) in the previous scan. The mean radiation dose reduction between scans was 89%. On follow-up, all VLD-CT findings were confirmed. CONCLUSION: A modified imaging protocol is applicable for confirmation of stone presence and location by utilizing very low-dose radiation exposure.

Revolution spectral CT for urinary stone with a single/mixed composition in vivo: a large sample analysis.

PURPOSE: To analyze various compositions of urinary stones using revolution spectral CT (rapid kV switching dual-energy CT) in vivo. METHODS: 202 patients with urinary stones underwent spectral CT before surgery. Zeff peak, overall scope and CT values were detected. Moreover, water/iodine attenuating material images were obtained. Removed stones were subjected to infrared spectroscopy after surgery. The results of infrared spectroscopy were compared with CT. RESULTS: 28 stones (14.08%) with single composition, 165 stones with two mixed compositions (81.68%), and 9 stones with three mixed compositions (4.46%) were observed. When Zeff peaks of stones with single/mixed compositions were summarized together, 146 peaks of calcium oxalate monohydrate, 119 peaks of calcium oxalate dihydrate, 55 peaks of carbapatite, 38 peaks of urate, 16 peaks of struvite, and 11 peaks of brushite were totally observed. 93.8% of calcium oxalate monohydrate had Zeff peaks between 13.3 and 14.0. 91.6% of calcium oxalate dihydrate had peaks between 12.0 and 13.3. For carbapatite, 90.9% of stones had peaks from 14.0 to 15.0. A total of 94.8% of urate had peaks between 7.0 and 11.0. 93.8% of struvite had peaks between 11.0 and 13.0, and 90.9% of brushite had peaks between 12.0 and 14.0. Moreover, densities of urate, struvite and brushite were low density in iodine-based images and high-density in water-based images. CONCLUSION: The in-vivo analysis of spectral CT in urinary stone revealed characteristics of different compositions, especially mixed compositions. An in-vivo predictive model may be constructed to distinguish stone compositions.

Non-contrast computed tomography characteristics in a large cohort of cystinuria patients.

PURPOSE: Cystine stones are widely considered hard and difficult to treat. Hounsfield Units (HU) are used in other stone types to estimate 'hardness' and treatments based on that finding. Our objective was to report mean HU of cystine stones in vivo in a large case series of cystinuria patients and assess for differences in genotype. METHODS: A prospective case series of cystinuria patients referred to a specialist centre was analysed. CT imaging was assessed by two independent radiologists to determine in vivo attenuation of cystine calculi. Mean HU was compared for both cystinuria genes (SLC3A1 and SLC7A9) using an independent t-test. RESULTS: 164 adult cystinuric patients were identified (55% male), median age 43 years (range 18-80). Median follow up was 31 months (IQR 10-62). Genetic data available for 153/164 (93%) demonstrated 97 SLC3A1 (63%) and 55 (36%) SLC7A9 mutations (39 homozygous, 16 heterozygous) and one heterozygous for both SLC3A1/SLC7A9. 107 patients had CT images available demonstrating calculi. Median HU across the cohort was 633 (5th to 95th centile 328-780). There was no difference in mean HU between SLC3A1 and SLC7A9 genotypes (p = 0.68) or homo and heterozygous SLC7A9 (p = 0.70). Mean HU correlated with stone size (Pearson correlation coefficient = 0.51, p < 0.001). CONCLUSION: In this large single centre cystinuria cohort, mean HU was low for stones that are difficult to treat. Calculi of < 800 HU should prompt consideration of a cystinuria diagnosis. Attenuation was not associated with genotype, and distinct 'smooth' and 'rough' stones were not observed. Calculi with HU > 1000 are unlikely pure cystine, and in a known cystinuric would suggest conversion to another stone type.

Single-energy CT predicts uric acid stones with accuracy comparable to dual-energy CT-prospective validation of a quantitative method.

OBJECTIVES: To prospectively validate three quantitative single-energy CT (SE-CT) methods for classifying uric acid (UA) and non-uric acid (non-UA) stones. METHODS: Between September 2018 and September 2019, 116 study participants were prospectively included in the study if they had at least one 3-20-mm urinary stone on an initial urinary tract SE-CT scan. An additional dual-energy CT (DE-CT) scan was performed, limited to the stone of interest. Additionally, to include a sufficient number of UA stones, eight participants with confirmed UA stone on DE-CT were retrospectively included. The SE-CT stone features used in the prediction models were (1) maximum attenuation (maxHU) and (2) the peak point Laplacian (ppLapl) calculated at the position in the stone with maxHU. Two prediction models were previously published methods (ppLapl-maxHU and maxHU) and the third was derived from the previous results based on the k-nearest neighbors (kNN) algorithm (kNN-ppLapl-maxHU). The three methods were evaluated on this new independent stone dataset. The reference standard was the CT vendor's DE-CT application for kidney stones. RESULTS: Altogether 124 participants (59 ± 14 years, 91 men) with 106 non-UA and 37 UA stones were evaluated. For classification of UA and non-UA stones, the sensitivity, specificity, and accuracy were 100% (37/37), 97% (103/106), and 98% (140/143), respectively, for kNN-ppLapl-maxHU; 95% (35/37), 98% (104/106), and 97% (139/143) for ppLapl-maxHU; and 92% (34/37), 94% (100/106), and 94% (134/143) for maxHU. CONCLUSION: A quantitative SE-CT method (kNN-ppLapl-maxHU) can classify UA stones with accuracy comparable to DE-CT. KEY POINTS: • Single-energy CT is the first-line diagnostic tool for suspected renal colic. • A single-energy CT method based on the internal urinary stone attenuation distribution can classify urinary stones into uric acid and non-uric acid stones with high accuracy. • This immensely increases the availability of in vivo stone analysis.

Computer-aided diagnosis with a convolutional neural network algorithm for automated detection of urinary tract stones on plain X-ray.

BACKGROUND: Recent increased use of medical images induces further burden of their interpretation for physicians. A plain X-ray is a low-cost examination that has low-dose radiation exposure and high availability, although diagnosing urolithiasis using this method is not always easy. Since the advent of a convolutional neural network via deep learning in the 2000s, computer-aided diagnosis (CAD) has had a great impact on automatic image analysis in the urological field. The objective of our study was to develop a CAD system with deep learning architecture to detect urinary tract stones on a plain X-ray and to evaluate the model's accuracy. METHODS: We collected plain X-ray images of 1017 patients with a radio-opaque upper urinary tract stone. X-ray images (n = 827 and 190) were used as the training and test data, respectively. We used a 17-layer Residual Network as a convolutional neural network architecture for patch-wise training. The training data were repeatedly used until the best model accuracy was achieved within 300 runs. The F score, which is a harmonic mean of the sensitivity and positive predictive value (PPV) and represents the balance of the accuracy, was measured to evaluate the model's accuracy. RESULTS: Using deep learning, we developed a CAD model that needed 110 ms to provide an answer for each X-ray image. The best F score was 0.752, and the sensitivity and PPV were 0.872 and 0.662, respectively. When limited to a proximal ureter stone, the sensitivity and PPV were 0.925 and 0.876, respectively, and they were the lowest at mid-ureter. CONCLUSION: CAD of a plain X-ray may be a promising method to detect radio-opaque urinary tract stones with satisfactory sensitivity although the PPV could still be improved. The CAD model detects urinary tract stones quickly and automatically and has the potential to become a helpful screening modality especially for primary care physicians for diagnosing urolithiasis. Further study using a higher volume of data would improve the diagnostic performance of CAD models to detect urinary tract stones on a plain X-ray.

[A Case of Upper Urinary Tract Stone in an Infant].

A 238-day-old female infant (122 days of age corrected for prematurity, weight 4,847 g) presented with macrohematuria. She was born at 23 weeks and3 days of pregnancy. Her birth weight was 492 g. Ultrasound revealeda 3×2 mm left ureteral stone and left hydronephrosis of grade I-II in the Society of Fetal Urology (SFU) Classification. She suffered from frequent vomiting and weight loss, and was treated with analgesics and rehydration. Eventually, left hydronephrosis was relieved, and she passed the stone at 271 days of age. The stone was 4×3×2 mm in size, and consisted of 98% calcium oxalate and2% calcium phosphate. No recurrent stone has been found during follow-up.

Substantial radiation dose reduction with consistent image quality using a novel low-dose stone composition protocol.

PURPOSE: To assess a novel low-dose CT-protocol, combining a 150 kV spectral filtration unenhanced protocol (Sn150 kVp) and a stone-targeted dual-energy CT (DECT) in patients with urolithiasis. METHODS: 232 (151 male, 49 ± 16.4 years) patients with urolithiasis received a low-dose non-contrast enhanced CT (NCCT) for suspected urinary stones either on a third-generation dual-source CT system (DSCT) using Sn150 kVp (n = 116, group 1), or on a second-generation DSCT (n = 116 group 2) using single energy (SE) 120 kVp. For group 1, a subsequent dual-energy CT (DECT) with a short stone-targeted scan range was performed. Objective and subjective image qualities were assessed. Radiation metrics were compared. RESULTS: 534 stones (group 1: n = 242 stones; group 2: n = 292 stones) were found. In group 1, all 215 stones within the stone-targeted DECT-scan range were identified. DE analysis was able to distinguish between UA and non-UA calculi in all collected stones. 11 calculi (5.12%) were labeled as uric acid (UA) while 204 (94.88%) were labeled as non-UA calculi. There was no significant difference in overall Signal-to-noise-ratio between group 1 and group 2 (p = 0.819). On subjective analysis both protocols achieved a median Likert rating of 2 (p = 0.171). Mean effective dose was significantly lower for combined Sn150 kVp and stone-targeted DECT (3.34 ± 1.84 mSv) compared to single energy 120 kVp NCCT (4.45 ± 2.89 mSv) (p < 0.001), equaling a 24.9% dose reduction. CONCLUSION: The evaluated novel low-dose stone composition protocol allows substantial radiation dose reduction with consistent high diagnostic image quality.

Frontiers of stone management.

PURPOSE OF REVIEW: The surgical tool-box for urinary stone disease is growing. In this review, we discuss recent developments in the surgical management of urolithiasis, with emphasis on tailoring the management to the individual patient, and attention to the quality of care. RECENT FINDINGS: Shockwave lithotripsy remains a popular noninvasive treatment option for patients, with new data emerging on how to improve treatment outcomes as well as its limitations. Next-generation holmium lasers are expanding the role of dusting techniques for ureteroscopy but further studies are needed to assess safety and clinical outcomes. Advances in miniaturization and patient positioning are propelling percutaneous renal stone surgery toward endoscopic combined intrarenal and simultaneous bilateral surgery for patients with complex stone disease. SUMMARY: Stone management will be increasingly personalized to the unique qualities of the patient, stone, desired outcome, and available expertise and technology. Future studies assessing the quality of stone surgery should incorporate objective metrics to better delineate the success and cost of the different techniques available.

Burst wave lithotripsy and acoustic manipulation of stones.

PURPOSE OF REVIEW: Burst wave lithotripsy and ultrasonic propulsion of kidney stones are novel, noninvasive emerging technologies to separately or synergistically fragment and reposition stones in an office setting. The purpose of this review is to discuss the latest refinements in technology, to update on testing of safety and efficacy, and to review future applications. RECENT FINDINGS: Burst wave lithotripsy produced consistent, small passable fragments through transcutaneous applications in a porcine model, while producing minimal injury and clinical trials are now underway. A more efficient ultrasonic propulsion design that can also deliver burst wave lithotripsy effectively repositioned 95% of stones in 18 human participants (18 of 19 kidneys) and clinical trials continue. Acoustic tractor beam technology is an emerging technology with promising clinical applications through the manipulation of macroscopic objects. SUMMARY: The goal of the reviewed work is an office-based system to image, fragment, and reposition urinary stones to facilitate their natural passage. The review highlights progress in establishing safety, effectiveness, and clinical benefit of these new technologies. The work is also anticipating challenges in clinical trials and developing the next generation of technology to improve on the technology as it is being commercialized today.

Artificial intelligence in the diagnosis, treatment and prevention of urinary stones.

PURPOSE OF REVIEW: There has a been rapid progress in the use of artificial intelligence in all aspects of healthcare, and in urology, this is particularly astute in the overall management of urolithiasis. This article reviews advances in the use of artificial intelligence for the diagnosis, treatment and prevention of urinary stone disease over the last 2 years. Pertinent studies were identified via a nonsystematic review of the literature performed using MEDLINE and the Cochrane database. RECENT FINDINGS: Twelve articles have been published, which met the inclusion criteria. This included three articles in the detection and diagnosis of stones, six in the prediction of postprocedural outcomes including percutaneous nephrolithotomy and shock wave lithotripsy, and three in the use of artificial intelligence in prevention of stone disease by predicting patients at risk of stones, detecting the stone type via digital photographs and detecting risk factors in patients most at risk of not attending outpatient appointments. SUMMARY: Our knowledge of artificial intelligence in urology has greatly advanced in the last 2 years. Its role currently is to aid the endourologist as opposed to replacing them. However, the ability of artificial intelligence to efficiently process vast quantities of data, in combination with the shift towards electronic patient records provides increasingly more 'big data' sets. This will allow artificial intelligence to analyse and detect novel diagnostic and treatment patterns in the future.

Fluoroless Endourological Surgery for Stone Disease: a Review of the Literature-Tips and Tricks.

PURPOSE OF REVIEW: Urologists are at significant risk due to radiation exposure (RE) from endourological procedures for stone disease. Many techniques described have shown a reduction of RE. The purpose of this article is to review available protocols to decrease RE during such procedures and provide tips and tricks for their implementation. RECENT FINDINGS: Several low-radiation and radiation-free protocols for percutaneous nephrolithotomy and flexible ureteroscopy have been described as an attempt to reduce RE during surgery. Beginning with specific checklists to ensure adequate C-arm usage, fluoroless procedures are based on endoscopic assessment, tactile guidance, and use of ultrasound to avoid fluoroscopy. A specific preoperative checklist and low radiation or complete fluoroless radiation endourological procedures have shown to be effective, feasible, and safe. It is recommended for urologists to be aware of the risks of RE and apply the "ALARA" (As Low As Reasonably Achievable) protocols.

CT, US and MRI of xanthine urinary stones: in-vitro and in-vivo analyses.

BACKGROUND: Xanthine urinary stones are a rare entity that may occur in patients with Lesch-Nyhan syndrome receiving allopurinol. There is little literature describing imaging characteristics of these stones, and the most appropriate approach to imaging these stones is therefore unclear. We performed in-vitro and in-vivo analyses of xanthine stones using computed tomography (CT) at different energy levels, ultrasound (US), and magnetic resonance imaging (MRI). METHODS: Five pure xanthine stones from a child with Lesch-Nyhan were imaged in-vitro and in-vivo. CT of the stones was performed at 80 kVp, 100 kVp, 120 kVp and 140 kVp and CT numbers of the stones were recorded in Hounsfield units (HU). US of the stones was performed and echogenicity, acoustic shadowing and twinkle artifact were assessed. MRI of the stones was performed and included T2-weighted, ultrashort echo-time-weighted and T2/T1-weighted 3D bFFE sequences and signal was assessed. RESULTS: In-vitro analysis on CT demonstrated that xanthine stones were radiodense and the average attenuation coefficient did not differ with varying kVp, measuring 331.0 ± 51.7 HU at 80 kVp, 321.4 ± 63.4 HU at 100 kVp, 329.7 ± 54.2 HU at 120 kVp and 328.4 ± 61.1 HU at 140 kVp. In-vivo analysis on CT resulted in an average attenuation of 354 ± 35 HU. On US, xanthine stones where echogenic with acoustic shadowing and twinkle artifact. On MRI, stones lacked signal on all tested sequences. CONCLUSION: Xanthine stone analyses, both in-vitro and in-vivo, demonstrate imaging characteristics typical of most urinary stones: dense on CT, echogenic on US, and lacking signal on MRI. Therefore, the approach to imaging xanthine stones should be comparable to that of other urinary stones.

An unusual complication of urinary stone disease: Spontaneous ureteral rupture.

Spontaneous ureter rupture (SUR) is a rare condition that can present mimicking many acute abdominal diseases. There is no probable explanation of the theoretical mechanism of spontaneous ureter rupture in the literature. Mostly calculus can cause spontaneous ureter rupture with ureteral obstruction. If left untreated, it can lead to life-threatening complications. Herein we report a case of SUR that was occurred due to urinary stone disease in a male patient who presented with abdominal pain. A non-contrast enhanced and a late phase contrast-enhanced abdominal computed tomography (CT) demonstrated the urinary stone disease and contrast extralumination from the proximal part of the left ureter. We also aimed to emphasize the clinical and CT imaging findings of SUR.