Urinary Stone Composition Research Articles

Kidney Stones of Type I vs. Type II Diabetic Patients: Are There Any Differences?

Background: This study highlighted the differences between the biochemical compositions of urinary stones from patients with type 1 diabetes versus those with type 2 diabetes. Materials and Methods: This study included patients diagnosed with kidney stones and diabetes who were referred to the Urological Clinic of the Dr. C. I. Parhon Hospital in Iasi from April 2017 to April 2024. We analyzed the spectroscopic stone composition from 128 lithiasis patients treated in our Clinic. In the current study, the distribution of the biochemical composition of stones varied significantly between diabetic patients with type 2 diabetes, who formed primarily mixed uric acid stones, and diabetic patients with type 1 diabetes, who mainly developed pure uric acid stones (p < 0.001). Patients with uric acid stones had significantly higher mean creatinine values than the other stone types (p < 0.001). Urinary pH levels were abnormal for all biochemical subtypes of stones, indicating acidic urine. However, patients with uric acid stones had lower pH values than the group average. From the Kaplan–Mayer analysis, patients with pure uric acid stones had a shorter time to stone recurrence compared to patients with other biochemical types identified. Conclusions: These findings, which highlight the prevalence of pure uric acid stones in patients with type 1 diabetes and the impact of this on the strategy for dissolving pure stones, represent a significant advancement in understanding urinary lithiasis in diabetic patients.

Decoding Urinary Stones: Compositional Insights and Recurrence Patterns from a Tertiary Care Hospital in Eastern India.

Background and objective Urinary stones are a prevalent medical condition entailing significant health burdens and substantial financial ramifications. Its global prevalence is expected to rise notably, particularly in low-middle-income nations. Understanding the spectrum of diverse urinary stone types is crucial for effective management and prevention. This study aims to elucidate the demographic profiles, clinical types, and recurrence of urinary stone cases at a tertiary care hospital in Eastern India. Methods and materials The clinical data from the electronic medical record of 1,231 patients with urolithiasis who underwent surgery in a tertiary care center in eastern India from January 2015 to November 2022 were retrospectively analyzed. Patient data, including demographic information, clinical records, dietary habits, water intake, and stone recurrence history, were collected. A thorough statistical analysis was conducted to elucidate the associations between patient characteristics, urinary stone composition, and recurrence. Results Among the 1,231 participants, the majority of cases (343 (27.9%)) were in the 31-40 age group, with a higher prevalence in males (876 (71.2%)) than in females (355 (28.8%)). Flank pain or abdominal pain (593 (48.1%)) was the primary complaint, followed by nausea/vomiting (227 (18.5%)). Most stones (922 (74.9%)) were located in the kidney, and calcium oxalate was the predominant stone component (773 (62.8%)). The highest stone recurrence rates were in patients aged between 21-30 years (21 (36.8%)) and 31-40 years (16 (28.0%)). A low non-vegetarian diet and adequate hydration may reduce the likelihood of stone recurrences. Conclusion This study provides insights into the compositional analysis of urinary stones in the eastern Indian population, addressing the causes of their recurrence and management strategies, which are crucial for prevention and effective management. The findings indicate that the early middle-aged group exhibited the highest incidence of urinary stones. We also observed that strict adherence to a low animal protein, along with proper hydration and lifestyle changes, significantly reduced urinary stone recurrence.

Refining Predictive Models for Urolithiasis: Methodological Insights and Clinical Implications.

We have reviewed the article "Predictive Modeling of Urinary Stone Composition Using Machine Learning and Clinical Data: Implications for Treatment Strategies and Pathophysiological Insights" by Chmiel et al. with keen interest. The authors have made significant strides in leveraging machine learning to predict urinary stone composition, a crucial factor in the management and treatment of urolithiasis. While the study presents innovative methodologies and insightful findings, there are several areas where the approach and interpretation could be refined to enhance the robustness and applicability of the results.

Investigation and quantification of composition variability in urinary stone analysis.

To investigate the variability in urinary stone composition analysis due to sampling and suggest potential solutions. We collected 1,135 stone fragments from 149 instances that had undergone a stone removal at Hanoi Medical University Hospital from January 2022 to August 2022. Each fragment was ground into fine powder and divided into separate specimens if the amount was abundant. For composition analyzing every specimen, Fourier transform infrared spectroscopy was performed. The composition of a given fragment was the average of its belonging specimens. The variability in composition was assessed on the fragment level (i.e., between fragments of an instance). We defined an instance as "significantly variable" if the maximum difference in any composition across its belonging fragments was equal to or greater than a given threshold. On average, there were 7.6±3.3 stone fragments per instance and 2.3±0.5 specimens per fragment. We found that the variability could be substantial on the fragment level. Eighty-nine (69.5%) and 70 (54.7%) out of 128 multiple-component instances were significantly variable if the threshold was set at 20% and 30%, respectively. The variability of an instance on the fragment level was correlated with the size of fragment and the number of components. Our study demonstrated the significant variability in urinary stone composition and showed that it correlated with the size and the impurity of samples. Mapping denotation while sampling and analyzing as well as reporting the composition of individual fragments could be valuable to reduce potential variability.

Demonstrating the Efficacy of Dual Energy Computer Tomography with Gemstone Spectral Imaging Software to Determine Mixed and Single Composition ex vivo Urolithiasis.

To assess the capability of determining the mixed chemical composition of urinary stones using spectral imaging properties of Dual Energy Computed Tomography (DECT) Gemstone Spectral Imaging (GSI) software. Twenty-six single and 24 mixed composition ex vivo urinary stones with known chemical composition determined by Fourier-transform infrared spectroscopy (FTIR) prior to this project were scanned with DECT imaging and GSI in vitro. The major components of the stones included Uric Acid (UA), Calcium Oxalate (CaOx), Calcium Phosphate (CaP), Magnesium Ammonium Phosphate (MAP), and Cystine (Cys). A histogram to display the distribution of the effective atomic number (Z-eff) of each pixel of the tested area, spectral curve (40-140 keV, with 10 keV interval) and Hounsfield Units (HU) of each stone scanned was provided with analysis of monochromatic images at 140 keV in the axial plane. The overall pooled sensitivity, specificity, and accuracy of DECT for identifying major stone composition were 0.802, 0.831, and 0.807, respectively, with a 95% confidence interval. Accuracy was 100% for identifying UA and Cys stones. DECT is a superior imaging modality when compared to low dose computed tomography kidney ureter bladder scans. It allows for improved characterization of major components of urinary stones, in an accurate, non-invasive approach to pre-treatment. This can translate to urologists having greater confidence in determining patient suitability for medical or surgical management of their renal stones, in clinical practice.

Determination of the kidney stone composition using infrared spectroscopy in Iran at a national referral center during 2019–2023

ObjectiveThe national lifetime prevalence of urolithiasis is estimated at 6.6% in Iran. However, reports on the composition of kidney stones have been based on imprecise methods like chemical analysis. No prior large-scale study has reported the composition of kidney stones based on the gold-standard methods (X-ray diffraction or infrared spectroscopy) in Iran. This study aimed to provide the composition of kidney stones based on Fourier transform infrared. MethodsThis is a cross-sectional study assessing urinary stone composition from various cities in Iran at a referral center using infrared spectroscopy from February 2019 to March 2023. ResultsThis study determined the stone composition of 1092 samples from 10 cities or provinces in Iran. Overall, the majority of stones were composed of calcium (n=522; 47.8%), uric acid (UA, n=488; 44.7%) followed by cystine (n=49; 4.5%), and struvite (n=28; 2.6%). Stone composition in Shiraz and Isfahan was roughly similar with a higher percentage of UA stones (53.4% and 53.6%, respectively) while the capital city of Iran (Tehran) had less frequent UA stones (39.9%) with a higher percentage of calcium oxalate stones. The percentage of UA stones increased with age as it was 11.1% in children, 42.7% in adults, and 83.3% in geriatric patients (p<0.001). About 29.6% of cystine stones were observed in children. ConclusionThe most frequent stone composition among kidney stones in Iran were calcium oxalate and UA stones. This relative frequency of UA stones is considerably higher than many international reports from neighboring as well as distant countries. More cystine stones were observed in children and women. Geriatric patients’ stones were mostly composed of UA.

Assessment of Urinary Stone Chemical Compositions and Prevalence of Metabolic Disorders Among Urolithiasis Patients in Northern Sri Lanka: A Prospective Study.

Introduction Urolithiasis, a common urological disorder affecting the global population, demonstrates geographical diversity due to factors such as water quality, climate variations, health conditions, and dietary habits. This study, conducted in Northern Sri Lanka, examines urinary stone compositions and assesses the prevalence of metabolic disorders among urolithiasis patients. Methods This prospective cross-sectional study, conducted at Jaffna Teaching Hospital, Jaffna, Sri Lanka, from July 2022 to June 2023, focused on surgically treated urolithiasis patients. Institutional ethical clearance was obtained. Patient details and investigational findings were collected through questionnaires and data extraction forms. Stone analysis utilized Fourier transform infrared spectroscopy, and a detailed metabolic evaluation of a 24-hour urine collection sample was carried out. Results This study followed 153 surgically treated urolithiasis patients, primarily male (64.3%), with a mean age of 48.64. Ureteric colic (48.4%) was common, with kidney stones (45.8%) prevalent; 57.52% had recurrent stones. Diabetes mellitus (DM; 23.5%) was the top comorbidity. Calcium oxalate monohydrate (COM) stones (78.4%) were the most frequent, followed by uric acid (12.4%). COM predominated in the 40-59 age group. There was no significant gender-stone type association. A total of 86.9% had metabolic abnormalities, notably hypocitraturia (60.1%). Moreover, 23% had both hypocitraturia and hypomagnesuria. Some metabolic disorders showed gender differences, with a marginal age-metabolic disorder association (p < 0.061). Urine oxalate levels were normal, with higher variability in males. Conclusion Middle-aged males with urolithiasis commonly presented with ureteric colic and predominantly had COM stones. Recurrent stones were common, often accompanied by metabolic abnormalities such as hypocitraturia and hypomagnesuria, with DM as the primary comorbidity.

The key role of major and trace elements in the formation of five common urinary stones

BackgroundUrolithiasis has emerged as a global affliction, recognized as one of the most excruciating medical issues. The elemental composition of stones provides crucial information, aiding in understanding the causes, mechanisms, and individual variations in stone formation. By understanding the interactions between elements in various types of stones and exploring the key role of elements in stone formation, insights are provided for the prevention and treatment of urinary stone disease.MethodsThis study collected urinary stone samples from 80 patients in Beijing. The chemical compositions of urinary stones were identified using an infrared spectrometer. The concentrations of major and trace elements in the urinary stones were determined using Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES) and Inductively Coupled Plasma Mass Spectrometry (ICP-MS), respectively. The data were processed using correlation analysis and Principal Component Analysis (PCA) methods.ResultsUrinary stones are categorized into five types: the calcium oxalate (CO) stone, carbonate apatite (CA) stone, uric acid (UA) stone, mixed CO and CA stone, and mixed CO and UA stone. Ca is the predominant element, with an average content ranging from 2.64 to 27.68% across the five stone groups. Based on geochemical analysis, the high-content elements follow this order: Ca > Mg > Na > K > Zn > Sr. Correlation analysis and PCA suggested significant variations in the interactions between elements for different types of urinary stones. Trace elements with charges and ionic structures similar to Ca may substitute for Ca during the process of stone formation, such as Sr and Pb affecting the Ca in most stone types except mixed stone types. Moreover, the Mg, Zn and Ba can substitute for Ca in the mixed stone types, showing element behavior dependents on the stone types.ConclusionThis study primarily reveals distinct elemental features associated with five types of urinary stones. Additionally, the analysis of these elements indicates that substitutions of trace elements with charges and ion structures similar to Ca (such as Sr and Pb) impact most stone types. This suggests a dependence of stone composition on elemental behavior. The findings of this study will enhance our ability to address the challenges posed by urinary stones to global health and improve the precision of interventions for individuals with different stone compositions.

Predictive Modeling of Urinary Stone Composition Using Machine Learning and Clinical Data: Implications for Treatment Strategies and Pathophysiological Insights.

Purpose: Preventative strategies and surgical treatments for urolithiasis depend on stone composition. However, stone composition is often unknown until the stone is passed or surgically managed. Given that stone composition likely reflects the physiological parameters during its formation, we used clinical data from stone formers to predict stone composition. Materials and Methods: Data on stone composition, 24-hour urine, serum biochemistry, patient demographics, and medical history were prospectively collected from 777 kidney stone patients. Data were used to train gradient boosted machine and logistic regression models to distinguish calcium vs noncalcium, calcium oxalate monohydrate vs dihydrate, and calcium oxalate vs calcium phosphate vs uric acid stone types. Model performance was evaluated using the kappa score, and the influence of each predictor variable was assessed. Results: The calcium vs noncalcium model differentiated stone types with a kappa of 0.5231. The most influential predictors were 24-hour urine calcium, blood urate, and phosphate. The calcium oxalate monohydrate vs dihydrate model is the first of its kind and could discriminate stone types with a kappa of 0.2042. The key predictors were 24-hour urine urea, calcium, and oxalate. The multiclass model had a kappa of 0.3023 and the top predictors were age and 24-hour urine calcium and creatinine. Conclusions: Clinical data can be leveraged with machine learning algorithms to predict stone composition, which may help urologists determine stone type and guide their management plan before stone treatment. Investigating the most influential predictors of each classifier may improve the understanding of key clinical features of urolithiasis and shed light on pathophysiology.

Mixed stones: urinary stone composition, frequency and distribution by gender and age

Proper analysis of urinary stone composition is a cornerstone for diagnosis, targeted treatment and recurrence prevention of urolithiasis. The aim of this study was to determine the composition, frequency and distribution of mixed stones according to gender and age of patients. A total of 42,519 urinary stones from 30,311 men and 12,208 women submitted between January 2007 and December 2020 were studied. Most urinary calculi consisted of two components (50.9%), followed by stones of a single constituent (27.1%) and three-component stones (21.9%), while four-component stones were only rarely identified (0.1%). Among all stones, 49.8% consisted of whewellite (COM), weddellite (COD), and mixtures of COM and COD, 33.8% were pure carbonate apatite (CA) and mixtures of CA with COM and/or COD, while 7.6% were composed of uric acid anhydrous (UAA), uric acid dihydrate (UAD), and mixed UAA and UAD. The remaining 8.8% of calculi were rare single-component stones and rare mixtures of various constituents. The number of stone components was inversely associated with age (p < 0.001). The proportion of men decreased significantly with the number of stone constituents, from 3.01:1 for single-component stones to 1.0:1 for four-component urinary calculi (p < 0.001). The vast majority of urinary calculi consisted of two or more components in varying proportions. While age was inversely associated with the number of stone constituents, the proportion of women increased significantly from single-component to four-component urinary calculi. A significant proportion of mixed stones could present a challenge for diagnosis and targeted recurrence prevention.

Predicting stone composition via machine-learning models trained on intra-operative endoscopic digital images

ObjectivesThe aim of this study was to use deep learning (DL) of intraoperative images of urinary stones to predict the composition of urinary stones. In this way, the laser frequency and intensity can be adjusted in real time to reduce operation time and surgical trauma.Materials and methodsA total of 490 patients who underwent holmium laser surgery during the two-year period from March 2021 to March 2023 and had stone analysis results were collected by the stone laboratory. A total of 1658 intraoperative stone images were obtained. The eight stone categories with the highest number of stones were selected by sorting. Single component stones include calcium oxalate monohydrate (W1), calcium oxalate dihydrate (W2), magnesium ammonium phosphate hexahydrate, apatite carbonate (CH) and anhydrous uric acid (U). Mixed stones include W2 + U, W1 + W2 and W1 + CH. All stones have intraoperative videos. More than 20 intraoperative high-resolution images of the stones, including the surface and core of the stones, were available for each patient via FFmpeg command screenshots. The deep convolutional neural network (CNN) ResNet-101 (ResNet, Microsoft) was applied to each image as a multiclass classification model.ResultsThe composition prediction rates for each component were as follows: calcium oxalate monohydrate 99% (n = 142), calcium oxalate dihydrate 100% (n = 29), apatite carbonate 100% (n = 131), anhydrous uric acid 98% (n = 57), W1 + W2 100% (n = 82), W1 + CH 100% ( n = 20) and W2 + U 100% (n = 24). The overall weighted recall of the cellular neural network component analysis for the entire cohort was 99%.ConclusionThis preliminary study suggests that DL is a promising method for identifying urinary stone components from intraoperative endoscopic images. Compared to intraoperative identification of stone components by the human eye, DL can discriminate single and mixed stone components more accurately and quickly. At the same time, based on the training of stone images in vitro, it is closer to the clinical application of stone images in vivo. This technology can be used to identify the composition of stones in real time and to adjust the frequency and energy intensity of the holmium laser in time. The prediction of stone composition can significantly shorten the operation time, improve the efficiency of stone surgery and prevent the risk of postoperative infection.

Management of nephrolithiasis in the Middle East over a recent decade: A systematic review.

Nephrolithiasis is a prevalent issue around the globe, particularly in hot climates such as Saudi Arabia. This analysis's objectives were to investigate the characteristics of kidney stones in Eastern Saudi Arabia and to provide the following findings: urinary stone composition, gender and age distribution, seasonal variations in stone formation, coexisting diseases linked to stone development, and urinary stone incidence. With comparisons to European and American populations, the primary risk factors for nephrolithiasis in Asian populations are to be determined through this systematic review and meta-analysis. We synthesized data from 13 geographically different studies using a thorough literature search through PubMed, ScienceDirect, and ResearchGate following the Preferred Reporting Items of Systematic Reviews and Meta-Analyses criteria. Potential targets for specialized public health programs were highlighted by the elucidation of differences in health-care-seeking behavior and disparities in health-care access. The results of this systematic analysis give doctors, researchers, and policymakers a thorough understanding of the condition of nephrolithiasis care in Saudi Arabia today. In addition, to maximize the care of nephrolithiasis in this particular group, this review identifies information gaps and highlights the necessity of context-specific guidelines and future research initiatives. All things considered, this systematic review addresses the unique possibilities and problems that exist within the Saudi Arabian health-care sector while also adding to the worldwide conversation on nephrolithiasis.

The first epidemiology of urolithiasis in Northern Vietnam: Urinary stone composition, age, gender, season, and clinical features study.

The aim of this study is to analyze the compositions of urinary stones and investigate their distributions in different ages, genders, seasons, and clinical features of Northern Vietnamese patients. A total of 231 patients with urinary stones from Northern Vietnam were collected and analyzed composition from 1/2021-12/2022. For all patients, age, sex, stone location, stone side, urine pH, and hospitalized date (month) were collected. Kidney stones are more frequently found in men than women with the male: female urinary stones ratio in this study being 1.96:1. The highest stone prevalence appeared between 60 and 69 years old. The most common stone composition was calcium oxalate, followed by calcium phosphate, uric acid, struvite, and cysteine. Mix stones of CaOx and CaP were more prevalent than pure stones. Males submitted more CaOx, CaP, and UA stones, whereas females were susceptible to infectious stones. Stones were more frequently found on the left side of the upper urinary tract (51.9%) than on the right side (27.3%) and lower urinary tract (7.8%). Cultural tendency leads to a smaller number of stones during the Lunar new year (February), and Ghost month (August).

Retrospective analysis of urinary tract stone composition in a Chinese ethnic minority colony based on Fourier transform infrared spectroscopy

To analyze the relationship between the composition of urinary stones and various influencing factors in the Enshi region. We used FT-IR to examine the composition of 1092 stone samples. Combined with the relevant clinical materials, the data were analyzed using both one-dimensional statistical methods and multivariate statistical methods. The study included 1092 stone samples, classified as follows: 457 (41.8%) with a single component, 453 (41.5%) with two components, 149 (13.6%) with three components, and 33 (3.0%) with four components. Stones were categorized into five types: Calcium Oxalate (CaOx) (76.4%), carbapatite (CaP) (9.3%), Struvite (ST) (8.3%), Uric Acid (UA) (4.9%), and Others (1.0%). Age, gender, urinary tract infection (UTI), family history of urinary stones (FH), hyperuricemia (HUA) and stone location were significantly associated with stone type. Logistic regression revealed that females and UTI were relative risk factors for predicting CaP and ST, while FH and HUA were relative risk factors for predicting UA. Our study indicates that the overall composition of urinary tract stones in the Enshi region is consistent with that of the entire China. Additionally, the predisposing factors for stone formation vary in terms of gender, age, FH, UTI, hyperuricemia HUA, and stone location.

Pulsed thulium:YAG laser—ready to dust all urinary stone composition types? Results from a PEARLS analysis

PurposeTo evaluate whether stone dust can be obtained from all prevailing stone composition types using the novel pulsed thulium:YAG (p-Tm:YAG), including analysis of stone particle size after lithotripsy.MethodsHuman urinary stones of 7 different compositions were subjected to in vitro lithotripsy using a p-Tm:YAG laser with 270 µm silica core fibers (Thulio®, Dornier MedTech GmbH®, Wessling, Germany). A cumulative energy of 1000 J was applied to each stone using one of three laser settings: 0.1 J × 100 Hz, 0.4 J × 25 Hz and 2.0 J × 5 Hz (average power 10 W). After lithotripsy, larger remnant fragments were separated from stone dust using a previously described method depending on the floating ability of dust particles. Fragments and dust samples were then passed through laboratory sieves to evaluate stone particle count according to a semiquantitative analysis relying on a previous definition of stone dust (i.e., stone particles ≤ 250 µm).ResultsThe p-Tm:YAG laser was able to produce stone dust from lithotripsy up to measured smallest mesh size of 63 µm in all seven stone composition types. Notably, all dust samples from all seven stone types and with all three laser settings had high counts of particles in the size range agreeing with the definition stone dust, i.e., ≤ 250 µm.ConclusionThis is the first study in the literature proving the p-Tm:YAG laser capable of dusting all prevailing human urinary stone compositions, with production of dust particles ≤ 250 µm. These findings are pivotal for the broader future implementation of the p-Tm:YAG in clinical routine.

Predicting Urinary Stone Composition in Single-Use Flexible Ureteroscopic Images with a Convolutional Neural Network.

Background and Objectives: Analysis of urine stone composition is one of the most important factors in urolithiasis treatment. This study investigated whether a convolutional neural network (CNN) can show decent results in predicting urinary stone composition even in single-use flexible ureterorenoscopic (fURS) images with relatively low resolution. Materials and Methods: This study retrospectively used surgical images from fURS lithotripsy performed by a single surgeon between January 2018 and December 2021. The ureterorenoscope was a single-use flexible ureteroscope (LithoVue, Boston Scientific). Among the images taken during surgery, a single image satisfying the inclusion and exclusion criteria was selected for each stone. Cases were divided into two groups according to whether they contained any calcium oxalate (the Calcium group) or none (the Non-calcium group). From 506 total cases, 207 stone surface images were finally included in the study. In the CNN model, the transfer learning method using Resnet-18 as a pre-trained model was used, and only endoscopic digital images and stone classification data were input to achieve minimally supervised learning. Results: There were 175 cases in the Calcium group and 32 in the Non-calcium group. After training and validation, the model was tested using the test set, and the total accuracy was 81.8%. Recall and precision of the test results were 88.2% and 88.2% in the Calcium group and 60.0% and 60.0% in the Non-calcium group, respectively. The area under the receiver operating characteristic curve of the model, which represents its classification performance, was 0.82. Conclusions: Single-use flexible ureteroscopes have financial benefits but low vision quality compared with reusable digital flexible ureteroscopes. As far as we know, this is the first artificial intelligence study using single-use fURS images. It is meaningful that the CNN performed well even under these difficult conditions because these results can further expand the possibilities of its use.

Impact of body mass index on size and composition of urinary stones: a systematic review and meta-analysis.

Several studies have explored the impact of BMI on size and composition of urinary stones. Because there were controversies, a meta-analysis was necessary to be carried out to provide some evidence of the relationship of BMI and urolithiasis. PubMed, Medline, Embase, Web of Science databases, and the Cochrane Library were searched up to August 12th 2022 for eligible studies. The urolithiasis patients were summarized into two groups: BMI < 25 and ≥ 25 kg/m2. Summary weighted mean difference (WMD), relative risk (RR) and 95% confidence intervals (CI) were calculated through random effects models in RevMan 5.4 software. A total of fifteen studies involving 13,233 patients were enrolled in this meta-analysis. There was no significant correlation of BMI and size of urinary stone (WMD -0.13mm, 95% CI [-0.98, 0.73], p = 0.77). Overweight and obesity increased the risk of uric acid stones in both genders and in different regions (RR=0.87, [95% CI] = 0.83, 0.91, p<0.00001). There was a higher risk of calcium oxalate stones formation in overweight and obesity group in total patients (RR=0.95, [95% CI] = 0.91, 0.98, p = 0.006). The relationship of BMI and calcium phosphate was not observed in this meta-analysis (RR=1.12, [95% CI] = 0.98, 1.26, p = 0.09). Sensitivity analysis was performed and indicated similar results. The current evidence suggests a positive association between BMI and uric acid and calcium oxalate stones. It would be of great guiding significance to consider losing weight when treating and preventing urinary stones.

Autofluorescence spectral analysis for detecting urinary stone composition in emulated intraoperative ambient

The prevalence and disease burden of urolithiasis has increased substantially worldwide in the last decade, and intraluminal holmium laser lithotripsy has become the primary treatment method. However, inappropriate laser energy settings increase the risk of perioperative complications, largely due to the lack of intraoperative information on the stone composition, which determines the stone melting point. To address this issue, we developed a fiber-based fluorescence spectrometry method that detects and classifies the autofluorescence spectral fingerprints of urinary stones into three categories: calcium oxalate, uric acid, and struvite. By applying the support vector machine (SVM), the prediction accuracy achieved 90.28 % and 96.70% for classifying calcium stones versus non-calcium stones and uric acid versus struvite, respectively. High accuracy and specificity were achieved for a wide range of working distances and angles between the fiber tip and stone surface in an emulated intraoperative ambient. Our work establishes the methodological basis for engineering a clinical device that achieves real-time, in situ classification of urinary stones for optimizing the laser ablation parameters and reducing perioperative complications in lithotripsy.

The relationship between patients’ kidney stone type and demographics in Israel: analysis of 10 K patients

To analyze urinary stone composition in Israel and assess the effects of key demographic parameters (gender, age, socioeconomic status, ethnicity, medical history and geographic region) on stone composition. A retrospective review was conducted of stone analysis of 10,633 patients from an HMO Israeli database analyzed by a central laboratory from 2014 to 2019 and subjected to Fourier-transform infrared spectroscopy. Associations between stone composition and different demographic parameters were determined using the Chi-square test. Calcium oxalate (CaOx) monohydrate accounted for 51.9% of the stones. Of the total sample, 5776 stones had one single component (54%), whereas 4857 (46%) had mixed components. Men had a higher frequency of CaOx stones (89.6% vs. 85.6%), whereas women had a higher frequency of calcium phosphate, infection, and cystine stones (27.2%, 17.7%, and 0.9% vs. 17.2%, 7.5%, and 0.5%, respectively). Cystine stones were more abundant in Arabs (1.2% vs. 0.5% in the Jewish population). Lower socioeconomic status was associated with a higher prevalence of calcium phosphate, uric acid, and infection stones and a lower prevalence of CaOx stones. Uric acid stones were associated with medical conditions such as diabetes, hypertension, ischemic heart disease, and obesity (28.3%, 24.9%, 25.7%, and 22.6% vs. 9.6%, 8.4%, 12.3%, and 10.3%, respectively). Stone types were highly influenced by patients' demographics. COM was the most common stone component in either pure or complex form. UA stone prevalence was found to increase with age and was associated with medical conditions such as diabetes, hypertension, ischemic heart disease, and obesity.

PD34-07 CAN CONVOLUTIONAL NEURAL NETWORK BE APPLIED TO THE PREDICTION OF URINARY STONE COMPOSITION IN SINGLE-USE FLEXIBLE URETEROSCOPIC IMAGES?

You have accessJournal of UrologyCME1 Apr 2023PD34-07 CAN CONVOLUTIONAL NEURAL NETWORK BE APPLIED TO THE PREDICTION OF URINARY STONE COMPOSITION IN SINGLE-USE FLEXIBLE URETEROSCOPIC IMAGES? Kyung Tak Oh, Dae Young Jun, Jae Young Choi, Dae Chul Jung, and Joo Yong Lee Kyung Tak OhKyung Tak Oh More articles by this author , Dae Young JunDae Young Jun More articles by this author , Jae Young ChoiJae Young Choi More articles by this author , Dae Chul JungDae Chul Jung More articles by this author , and Joo Yong LeeJoo Yong Lee More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003327.07AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: This study investigated whether Convolutional neural network (CNN) can show decent results in predicting urinary stone composition even in single-use flexible ureterorenoscopic (fURS) images with relatively low resolution. METHODS: This study retrospectively used surgical videos of fURS performed by a single surgeon using single-use scope (LithoVue) between 2018 and 2021. Digital images of the surface of the stone before laser fragmentation were captured in the surgical full HD video clips. Cases were divided into two groups according to whether they contained any calcium oxalate (Calcium group) or not (Non-calcium group). Among total 512 cases, 207 of stone surface images were finally included. Among total images, 25 were first designated as the test set, then augmentation for Non-calcium group was performed. The train set and validation set were divided by random split in an 8 to 2 ratio. In the CNN model, Resnet-18 model was used, and only endoscopic digital images and stone classification data were input to achieve minimal supervised learning (Fig 1). RESULTS: There were 175 cases in the Calcium group, and 32 cases in the Non-calcium group. After whole training, in validation set, the total accuracy was 82.0%, and recall, specificity and precision for Calcium group was 80.0%, 83.3% and 76.2%, and for Non-calcium group, it was 83.3%, 80.0% and 86.2%, respectively. Area under ROC curve of the model, which represents the classification performance of the model, was 0.863. After training and validation, the model was tested using the test set, and the total accuracy was 84.0%. Recall, specificity and precision of the test result was 80.0%, 100.0% and 100.0% in Calcium group, and 100.0%, 80.0% and 55.6% in Non-calcium group, respectively (Fig 2). CONCLUSIONS: As far as we know, this study is the first artificial intelligence study using a single-use fURS images. Source of Funding: None. © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e922 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Kyung Tak Oh More articles by this author Dae Young Jun More articles by this author Jae Young Choi More articles by this author Dae Chul Jung More articles by this author Joo Yong Lee More articles by this author Expand All Advertisement PDF downloadLoading ...