Kidney stones are a prevalent urological condition with significant global burden, often diagnosed using ultrasound (US) as a first-line modality despite its limitations in sensitivity and operator dependency. Artificial intelligence (AI) and deep learning (DL) algorithms have shown promise in enhancing US-based kidney stone applications, including detection, classification, complication prediction, and procedural guidance, but evidence remains heterogeneous.
To systematically review and synthesize the applications of AI and DL algorithms in US-based kidney stone detection, classification, prediction of complications/outcomes, and procedural guidance.
This systematic review followed PRISMA guidelines (PROSPERO: CRD420251247650). Databases including PubMed, Embase, Scopus, and others were searched from inception without language restrictions. Eligible studies were original peer-reviewed articles evaluating AI/DL in US for kidney stone diagnostics against reference standards like CT or surgical findings. Two reviewers independently screened, extracted data, and assessed quality using QUADAS-2 with AI extensions.
From 1,285 records, 9 studies were included after exclusions. These encompassed DL for image detection/segmentation (nโ=โ3), predictive modeling for complications/outcomes (nโ=โ4), and procedural guidance (nโ=โ2). Methodologies included CNN variants and ML ensembles. Performance metrics were high, with accuracies up to 96.54%, AUCsโ>โ0.90 for predictions, and improved procedural outcomes. Risk of bias was low in most studies (5/9), with some concerns in others. Heterogeneity in datasets and validation limited meta-analysis.
AI and DL algorithms demonstrate high diagnostic accuracy and clinical utility in enhancing US for kidney stone management, with stratification by application type revealing high performance across tasks, addressing traditional limitations. However, methodological variability and low to very low certainty of evidence (per GRADE) necessitate standardized external validation and multimodal integration for broader adoption.
PURPOSE OF REVIEW: Urolithiasis management increasingly depends on accurate, noninvasive stone phenotyping to guide acute intervention, secondary prevention, and selective chemolitholysis. Photon-counting computed tomography (PCCT) introduces detector-level energy discrimination and higher spatial resolution, enabling calcium-preserving reconstruction strategies and quantitative spectral analytics that may shift stone characterization from a laboratory endpoint toward an imaging-derived biomarker. RECENT
Recent peer-reviewed PCCT studies have concentrated on three translational domains. First, calcium-preserving virtual non-iodine (VNI) and virtual non-contrast (VNC) reconstructions have been evaluated for upper-tract stone detection in contrast-enhanced settings, supporting the concept that a single contrast-enhanced acquisition could potentially replace multiphase protocols in selected scenarios. Second, comparative ex vivo and clinical imaging studies suggest that PCCT improves depiction of small calculi and enables automated, high-resolution stone burden quantification. Third, spectral radiomics and machine-learning models have been applied to monoenergetic PCCT reconstructions for multi-class stone composition discrimination, achieving high discriminatory performance in controlled ex vivo datasets, and complementary phantom work has demonstrated automated uric acid versus non-uric acid classification. The emerging literature suggests that PCCT may support calcium-preserving assessment of stones in contrast-enhanced imaging, automated and reproducible stone burden quantification, and composition phenotyping via spectral analytics. However, most studies remain phantom/ex vivo and highly platform-specific. Translation will depend on prospectively defined acquisition and reconstruction parameters, externally validated models, and rigorous reporting aligned with contemporary machine-learning standards.
Kidney stone disease is a prevalent urological disorder that can result in severe pain, obstruction, and long-term complications if not detected and managed promptly. Traditional diagnostic approaches, particularly those relying on manual assessment of ultrasound images, often suffer from limitations such as subjective interpretation, dependency on radiologist expertise, and challenges in identifying small or complex stones. These constraints can lead to diagnostic delays and inconsistencies, especially in time-sensitive or resource-limited clinical settings. Therefore, the need for an intelligent, automated solution that enhances diagnostic accuracy and efficiency is more critical than ever. To address these issues, we propose a novel deep learning-based model called the Kronecker Self-Organizing Map Forward Harmonic Network (KSOMFHNet) for kidney stone classification using ultrasound imagery. The model begins with an image preprocessing phase, where a double bilateral filter is applied to effectively denoise the ultrasound images. Following this, the Deep Recursive Residual Network (DRRN) is employed to segment the kidney region accurately. Feature extraction is then performed using a combination of Binary Robust Independent Elementary Features (BRIEF), shape-based features, and Gray Level Co-Occurrence Matrix (GLCM) texture descriptors. These features are then used for classification via the KSOMFHNet, a hybrid architecture integrating the Deep Kronecker Neural Network (DKN) and Self-Organizing Map Network (SOMNet). This fusion enhances the model's learning capacity and spatial representation abilities. Experimental results demonstrate that KSOMFHNet achieves high performance, with an accuracy of 91.984%, a True Positive Rate (TPR)โof 90.543%, a True Negative Rate (TNR)โof 92.248%, a precision of 90.179%, and an F1-score of 90.360% for training data is 90%, highlighting its potential for clinical deployment.
Urolithiasis remains a significant clinical burden, and accurately predicting stone-free status after retrograde intrarenal surgery (RIRS) is essential for optimizing patient counseling and surgical planning. This study compared the predictive performance of a machine learning-based Random Forest (RF) model with the T.O.HO. scoring system, a traditional prediction tool designed for flexible ureteroscopy. A total of 452 patients who underwent RIRS for renal or proximal ureteral calculi at a single tertiary center were retrospectively analyzed. Preoperative demographic and radiological variables were collected, and postoperative stone-free status was assessed at the third week using KUB radiography or non-contrast CT when clinically indicated. The T.O.HO. score was calculated for each patient. The RF model was trained on 70% of the dataset and tested on the remaining 30%, using only preoperative variables, with model optimization performed within the training set using 5-fold cross-validation, and predictive performance evaluated through accuracy, sensitivity, specificity, F1-score, and receiver operating characteristic (ROC) analysis. The T.O.HO. score showed limited but statistically significant discrimination in this cohort. Higher T.O.HO. values were associated with a lower likelihood of postoperative stone-free status, indicating an inverse relationship between the score and surgical success. When interpreted accordingly for stone-free status prediction, the T.O.HO. score yielded an AUC of 0.615 (95% CI 0.556-0.674). In contrast, the RF model showed higher apparent internally validated discrimination, achieving an AUC of 0.843, an accuracy of 87.5%, a sensitivity of 98.1%, and a specificity of 53.1%. Feature importance analysis indicated that stone thickness, preoperative nephrostomy, stone length, stone width, and age contributed most significantly to the model's predictive ability. Overall, these findings suggest that a machine learning-based approach may provide complementary individualized risk stratification for postoperative stone-free status after RIRS when used alongside established clinical judgment and existing scoring systems. However, this model was developed and evaluated using internal validation only, and external prospective validation is required before its clinical implementation can be considered.
Rapid and reliable detection of kidney stones on non-contrast abdominal CT is essential for timely decision-making in emergency radiology. However, rising imaging volumes and workflow pressures continue to limit reporting capacity, creating a need for AI systems capable of supporting routine diagnostic practice. Although many AI-based stone detection models have been proposed, most rely on retrospective datasets, and few have been evaluated prospectively within environments that reflect real radiology workflow conditions. This study prospectively evaluates the performance, usability, and workflow compatibility of a deep learning-based kidney stone detection model deployed within a web-based platform designed to emulate key components of routine radiology practice, enabling forward-in-time evaluation without direct integration into routine clinical operations such as PACS/RIS or clinical reporting. A dual-stage convolutional neural network was developed using an internal dataset of 235 cases (3,452 slices) and validated through five-fold patient-level cross-validation. An independent set of 732 slices served as an independent hold-out set. For prospective evaluation, the trained model was integrated into a secure, browser-based interface supporting case upload, slice-level review, independent radiologist labeling, and visualization of AI-generated predictions. Over a six-month period, three radiologists uploaded and annotated a total of 5,152 anonymized CT slices. The platform dynamically calculated diagnostic metrics and logged human-AI interactions to assess performance stability and concordance. The pilot deployment demonstrated strong diagnostic performance under real-world variability, achieving 97.83% accuracy, 94.64% sensitivity, 98.27% specificity, 88.50% precision, and a Cohen's kappa of 0.90. Concordance between radiologists and the model exhibited increasing stability across sequential pilot stages. These findings present a reproducible framework for transitioning radiological AI systems from retrospective validation toward workflow-aligned, prospective pilot deployment. Although full PACS/RIS integration was not attempted, the results underscore the importance of pilot-stage evaluation as a critical intermediary step toward clinical implementation and regulatory approval.
Axial gout is an underrecognized manifestation of monosodium urate (MSU) crystal deposition and frequently mimics inflammatory, infectious, or mechanical spinal disorders, particularly without overt hyperuricaemia. We report a 29-year-old man with recurrent low back pain for over two years and recurrent nephrolithiasis who had undergone three extracorporeal shock wave lithotripsy sessions with only transient relief. Admission laboratory investigations showed normal serum urate (262 ยตmol/L, ~โ4.4ย mg/dL) but markedly elevated Cโreactive protein (119ย mg/L) and erythrocyte sedimentation rate (53ย mm/h). Retrospective review of prior hospitalizations for ureteral stones revealed that routine serum urate measurements had never been elevated. Conventional imaging, including plain radiography and nonโcontrast computed tomography, was inconclusive. Dualโenergy computed tomography (DECT) revealed MSU deposition in the L4/L5 and L5/S1 facet joints and sacral foramina, supporting the diagnosis of axial gout. The patient was treated with etoricoxib (60ย mg daily) and febuxostat (20ย mg daily). On telephone followโup on 27 May 2026 (the only followโup to date), he reported that the J stent had been removed and low back pain had completely resolved without recurrence. A single repeat laboratory test performed locally was reported as normal, though specific values were unavailable. A systematic literature review of 58 eligible articles revealed that the lumbar spine was most frequently involved (โโ60%), DECT sensitivity ranged from 78% to 100%, and 77.5% of historically reported cases required surgical diagnosis, underscoring the underrecognition of axial gout in non-surgical settings. This caseโbased review highlights that axial gout should be considered in young nonโhyperuricaemic patients with persistent axial pain, and that DECT is a valuable nonโinvasive diagnostic tool when interpreted alongside clinical and laboratory features.
CONTEXT: Experimental evidence supporting the existence of the viscerosomatic reflex highlights an involvement of multiple vertebral levels when renal pathology is present. Further exploration of this reflex, particularly in the context of nephrolithiasis, could offer valuable insights for osteopathic treatments related to this pathology. Open-sourced machine learning datasets provide a valuable source of imaging data for investigating osteopathic phenomena including the viscerosomatic reflex.
This study aimed to compare the rotation of vertebrae at levels associated with the viscerosomatic reflex in renal pathology in patients with nephrolithiasis vs. those without kidney stones.
A total of 210 unenhanced computed tomography (CT) scans were examined from an open-sourced dataset designed for kidney and kidney stone segmentation. Among these, 166 scans were excluded due to pathologies that could affect analysis (osteophytes, renal masses, etc.). The 44 scans included in the analysis encompassed 292 relevant vertebrae. Of those, 15 scans were of patients with kidney stones in the right kidney, 13 in the left kidney, 7 bilaterally, and 11 without kidney stones. These scans included vertebral levels from T5-L5, with the majority falling within T10-L5. An open-sourced algorithm was employed to segment individual vertebrae, generating models that maintained their orientation in three-dimensional (3D) space. A self-coded 3D slicer module utilizing vertebral symmetry for rotation detection was then applied. Two-way analysis of variance (ANOVA) testing was conducted to assess differences in vertebral rotation between the four possible combinations of kidney stone location (left-sided, right-sided, bilateral, or none) and vertebral levels (T10-L4). Subsequently, the two-way ANOVA analysis was narrowed down to include various combinations of three vertebral levels (T10-L4) to identify the most significant levels.
We observed a statistically significant difference in average vertebral rotation (p=0.0038) dependent on kidney stone location. Post-hoc analysis showed an average difference in rotation ofย -1.38ยฐ leftward between scans that contained left kidney stones compared to no kidney stones (p=0.027), as well as an average difference ofย -1.72ยฐ leftward in the scans containing right kidney stones compared to no kidney stone (p=0.0037). The average differences in rotation between the remaining stone location combinations were not statistically significant. Narrowed analysis of three vertebral level combinations showed a single statistically significant combination (T10, T12, and L4) out of a total of 35 combinations (p=0.028). A subsequent post-hoc procedure showed that angular rotation at these levels had the only statistically significant contribution to the difference between scans containing right kidney stones and no kidney stones (p=0.046).
This study observed a statistically significant difference in the rotation of vertebrae at the levels associated with the viscerosomatic reflex between patients with unilateral kidney stones and those without kidney stones. The vertebral levels with the highest significance of association with this finding, particularly in right kidney stones, were T10, T12, andย L4.
Urolithiasis is a prevalent urological condition, and Non-Contrast Computed Tomography (NCCT) is the gold standard for diagnosis. In recent years, there has been growing interest in investigating machine learning (ML)- based detection of urolithiasis and the wider potential of AI in urology.
To synthesise the diagnostic accuracy of ML-based UTS detection on NCCT and in externally validated cohorts.
We performed a systematic review and bivariate meta-analysis of studies evaluating ML for detecting urinary stones. We used QUADAS-2 to assess the risk of bias. Subgroup analyses examined performance by model type, classification task, stone site, dataset source, and CT orientation. Bivariate meta-regression was performed to further explore heterogeneity. Publication bias was assessed using Deeks' test. The study was prospectively registered in Prospero (CRD42024542409).
Forty-five studies were included qualitatively. 24 studies (49,277 test images) provided extractable 2ย รย 2 data for meta-analysis. For NCCT (10 studies), pooled sensitivity was 96% (95% CI 92-98%) and pooled specificity was 98% (95% CI 97-99%). In externally validated NCCT cohorts (4 studies; 1,056 images), pooled sensitivity was 95% (95% CI 92-97%) and pooled specificity was 96% (95% CI 70-100%). Subgroup performance remained high, but heterogeneity persisted; meta-regression found stone site contributed to variability (pย =ย 0.014), while other moderators were not significant. Deeks' test showed no small-study effects (pย =ย 0.571).
ML models show high image-level diagnostic performance for stone detection on NCCT and may support radiologists as decision support tools. Translation is limited by heterogeneity and limited external validation. Future studies should move beyond detection-alone tasks towards clinically meaningful outputs that are actionable for radiologists and downstream clinicians, including urologists and nephrologists.
Three-dimensional (3D) rendering of urologic pathology plays an important role in simulation-based education, surgical training, and computer vision research; however, a standardized, open-access repository of high-fidelity kidney stone models stratified by chemical composition is lacking. We developed and validated a reproducible photogrammetry-based pipeline to generate realistic 3D kidney stone renderings. Chemically characterized human stones composed of calcium oxalate monohydrate (COM) (nโ=โ11), uric acid (UA) (nโ=โ5), cystine (nโ=โ4), magnesium ammonium phosphate hexahydrate/carbonate apatite (MAPH/CA) (nโ=โ2), and calcium hydrogen phosphate dihydrate (CHPD) (nโ=โ3) were photographed using a custom-built rotating stage and dual fixed 4ย K cameras. Rendered models were sent to 25 endourologists using a 5-point Likert-scale survey assessing geometric and surface texture fidelity. Successful 3D renderings were obtained for 8/11 COM stones, 5/5 UA stones, 2/2 MAPH/CA fragments, and 3/3 CHPD fragments, while all cystine stones failed to render. Across stone types, mean fidelity scores were highest for UA and COM stones (mean 3.8-3.9), intermediate for calcium phosphate stones (mean 3.6-3.8), and lowest for struvite stones (mean 3.0-3.3). Geometry scores were higher than texture scores overall, though this difference was not significant. Significant differences in geometric fidelity were observed across stone compositions (ฯยฒ = 9.30, pโ=โ0.026). Inter-rater reliability was poor for individual evaluators (ICCโ=โ0.10) but moderate for aggregated mean ratings (ICCโ=โ0.67). This validated workflow enables the creation of generally realistic, open-access 3D kidney stone models (github.com/uro-glidar/3d-rendering-diverse-stones) for simulation, education, and future machine learning applications in endourology.
Visceral adiposity has been implicated in metabolic dysregulation and chronic inflammation, both of which may contribute to kidney stone recurrence. However, accurate and reproducible quantification of visceral fat in routine clinical practice remains challenging. This study aimed to investigate the association between visceral fat area (VFA) quantified from computed tomography (CT) images and the risk of kidney stone recurrence. We retrospectively analyzed patients with urolithiasis who underwent abdominal CT imaging. Visceral fat area was automatically quantified using a previously validated artificial intelligence (AI)-based CT segmentation system. Clinical characteristics and stone recurrence outcomes were collected. Multivariable regression models were applied to assess the association between VFA and stone recurrence after adjustment for relevant confounders. A total of 131 patients were included, of whom 73 (48%) experienced stone recurrence during a mean follow-up of 47 weeks. Patients with recurrence had significantly higher visceral fat area. High VFA was independently associated with increased recurrence risk (adjusted hazard ratio [HR] 1.71, 95% confidence interval [CI] 1.03 to 2.82). Subgroup analyses demonstrated a stronger association in younger patients (HR 2.45, 95% CI 1.23 to 4.89), while no significant association was observed in older patients or across sexes. CT-derived visceral fat area was independently associated with kidney stone recurrence in this retrospective cohort. These findings suggest that visceral adiposity may serve as a useful imaging biomarker for risk stratification. Further prospective studies are warranted to validate its clinical utility.
Ceftriaxone-associated biliary pseudolithiasis is well reported, but the true risk of urolithiasis remains less clearly defined. Prior reviews frequently combine biliary and urinary tract calcifications or rely mainly on descriptive findings, limiting accurate incidence estimates.
This systematic review and meta-analysis aimed to provide the first pooled frequency estimate specifically for ceftriaxone-induced urolithiasis in children. DATA SOURCES: A systematic search of PubMed, Google Scholar, Web of Science, and Scopus was conducted up to November 2025. STUDY ELIGIBILITY CRITERIA: Studies reporting the cases of urolithiasis in pediatric patients receiving ceftriaxone were included. STUDY APPRAISAL AND DATA SYNTHESIS: Data extraction and synthesis were performed by two independent reviewers and analyzed by Stata 19.5. Pooled proportions were calculated using a random-effects model with REML estimation to determine overall frequency, between-study variance (ฯ2), and 95% prediction intervals (PI).
Eight studies met the inclusion criteria. The pooled frequency of ceftriaxone-induced urolithiasis was 7% (95% CI: 2-12%). Heterogeneity was substantial (I2โ=โ89.8%; ฯ2โ=โ0.0034), and the 95% PI ranged from 2.7 to 15.8%. Subgroup analyses showed that retrospective studies from Asian regions reported markedly higher rates (up to 34%), whereas prospective Western studies consistently demonstrated lower frequencies. Sensitivity analysis excluding the main outlier reduced the pooled estimate to 4% (pโ=โ0.004). Publication bias was detected (Egger's test, pโ=โ0.006), indicating underreporting of studies with low event rates. CONCLUSIONS AND IMPLICATIONS OF KEY
This meta-analysis suggests that ceftriaxone-associated urinary tract lithiasis occurs in approximately 7% of pediatric patients, with substantial variability driven by study design and diagnostic approach. Well-powered, prospective studies with standardized imaging protocols are required to define the true incidence and modifiable risk factors. SYSTEMATIC REVIEW REGISTRATION NUMBER: (CRD42024598001).
Accurate assessment of stone burden is fundamental in urolithiasis, as it directly influences treatment selection and prognostic evaluation. Although maximum stone diameter on non-contrast computed tomography remains the most widely used parameter, it does not adequately reflect the three-dimensional complexity of urinary calculi. This review aimed to summarize the evolution of stone burden assessment from conventional imaging-based methods to software-assisted volumetry and artificial intelligence (AI)-driven tools, with emphasis on their accuracy, reproducibility, and clinical utility.
A narrative review of the literature was performed using PubMed/MEDLINE, Scopus, and Google Scholar for English-language studies published up to March 2026. Original studies, validation studies, technical reports, reviews, and guideline-related papers addressing conventional CT-based measurement, software-assisted volumetry, AI-based stone segmentation, and the clinical significance of stone volume were included. Due to heterogeneity in study design and reported outcomes, the evidence was synthesized narratively.
Maximum stone diameter remains simple and widely available, but it incompletely represents true stone burden, particularly in larger or irregular stones. Formula-based ellipsoid calculations are practical yet show limited accuracy in complex geometries. Semi-automated CT-based segmentation software provides more reliable volumetric assessment, with excellent agreement with reference standards and reduced interobserver variability. AI-based approaches have further improved efficiency by enabling rapid and highly accurate automated stone detection and volume calculation. Across the reviewed literature, stone volume was consistently shown to be more clinically informative than linear dimensions for predicting spontaneous passage, stone-free rates after shockwave lithotripsy and ureteroscopy, and future symptomatic events during surveillance.
Stone volume offers a more accurate and clinically meaningful estimate of stone burden than maximum stone diameter alone. The transition from formula-based methods to software-assisted and AI-driven volumetry represents an important advance in urolithiasis imaging. Wider adoption will depend on standardized imaging protocols, improved software accessibility, and validation of volume-based thresholds for routine clinical practice.
PURPOSE OF REVIEW: Stone volume represents the most accurate measure of urolithiasis burden. While this may be obvious to all, this stone metric has not yet found its way into standard clinical practice or guidelines algorithms guiding treatment strategies. Although linear measurements have their obvious limitations, they are still most commonly used in practice and reported in literature as well as guidelines. This review evaluates the current available evidence supporting stone volume as the most important stone metric. RECENT
By now, literature has been able to confidently demonstrate that stone volume is a more accurate predictor of stone free status in shockwave lithotripsy and ureteroscopy. Recent advances in three-dimensional imaging reconstruction, automated segmentation and artificial intelligence have lowered thresholds for obtaining stone volume from computed tomography imaging. SUMMARY: Today, historical barriers have been overcome, and fast, reproducible stone volume assessment is at our fingertips. And yet, we do not use volume in our daily practice, as we ourselves hold back evolution. While future efforts should be put towards embracing volume as a stone metric, more studies are also needed to identify volume-based thresholds for treatment success aiding in the development of new treatment algorithms.
In low-invasive surgical treatment of urolithiasis, there is a need for an analytical method to determine the chemical composition of urinary stones in real-time mode, i.e., intraoperatively. While a thorough phase analysis can be done after the surgery, preliminary information about a target stone would be helpful for the specialists for choosing an optimal strategy of treatment and giving some immediate dietary or drug prescriptions to a patient. Near-infrared spectroscopy (NIRS) is a good candidate for such a method that can provide immediate results without obligatory sample preparation. Fiber optic probes, often used for acquiring near-infrared spectra, are compatible with surgical instrumentation. Chemometric algorithms can successfully resolve the complexity of NIR spectra, which consist of overlapped signals. For the first time, we applied NIRS in diffuse reflectance mode to classify three major types of urinary stones: oxalates, urates, and phosphates. To imitate the real conditions of a surgery, the NIR spectra were acquired not only under ambient conditions but also in saline medium. A trained and optimized multinomial classifier (Error Correcting Output Codes) showed an acceptable precision and recall for an independent validation dataset. Even considering the strong absorbance of saline, the calculated geometric mean was 94ย %, 87ย %, and 71ย % for oxalates, urates, and phosphates, respectively. A first real-time approbation during a real surgery (percutaneous nephrolithotomy) demonstrated a compatibility of the suggested approach with the surgical protocols and a good agreement of the acquired NIR spectra and the results of reference X-ray phase analysis.
In small animal practice, patients often present with urinary lithiasis, and prediction of urolith composition is essential to determine the appropriate treatment. Through abdominal radiographs, the composition of mineral radiopaque uroliths can be determined by considering many different factors; this can be complex and, as such, tailor-made for the use of artificial intelligence (AI). The Minnesota Urolith Center partnered with Hill's Pet Nutrition to develop a deep learning AI algorithm (CALCurad) within a smartphone application called the MN Urolith Application that allows for the preliminary assessment of urolith composition. The algorithm provides the probability of a urolith being composed of struvite from an image taken of an abdominal radiograph. This pilot study evaluates the accuracy of the CALCurad in the context of clinical practice. A sample population of 139 dogs was considered, and the results obtained by the CALCurad were compared with the results obtained by infrared spectroscopy analysis. Agreement between the application and quantitative analyses was 81.3%. These results suggest that the CALCurad can effectively be used to predict urolith composition in dogs, helping the clinician to decide between medical and surgical management of the patient. The use of the CALCurad is an example of the usefulness of AI in helping veterinarians make clinical decisions in patient care.
Diagnosing ureteral stones with low-dose CT in patients with metal hardware can be challenging because of image noise. The purpose of this study was to compare ureteral stone detection and image quality of low-dose and conventional CT scans with and without deep learning reconstruction (DLR) and metal artifact reduction (MAR) in the presence of metal hip prostheses.
Ten urinary system combinations with 4 to 6 mm ureteral stones were implanted into a cadaver with bilateral hip prostheses. Each set was scanned under two different radiation doses (conventional dose [CD] = 115 mAs and ultra-low dose [ULD] = 6.0 mAs). Two scans were obtained for each dose as follows: one with and another without DLR and MAR. Two blinded radiologists ranked each image in terms of artifact, image noise, image sharpness, overall quality, and diagnostic confidence. Stone detection accuracy at each setting was calculated.
ULD with DLR and MAR improved subjective image quality in all five domains (p < 0.05) compared with ULD. In addition, the subjective image quality for ULD with DLR and MAR was greater than the subjective image quality for CD in all five domains (p < 0.05). Stone detection accuracy of ULD improved with the application of DLR and MAR (p < 0.05). Stone detection accuracy of ULD with DLR and MAR was similar to CD (p > 0.25).
DLR with MAR may allow the application of low-dose CT protocols in patients with hip prostheses. Application of DLR and MAR to ULD provided a stone detection accuracy comparable with CD, reduced radiation exposure by 94.8%, and improved subjective image quality.
Background This study aims to identify the reliability of kidney-ureter-bladder (KUB) radiography as a triage tool in acute ureteral colic (AUC). Moreover, this article correlates between KUB and non-contrast computerized tomography (NCCT) in view of stone characteristics and clinical outcomes. Methodology A retrospective cohort study recruited patients who had proven ureteric stones on NCCT. A blinded review of KUB and NCCT was performed to identify the following variables in both tests: site, ureteric stone maximum diameter, and stone density. Correlation between KUB radiography and NCCT has been performed. The intermethod reliability was used to measure the degree to which test scores are consistent when the methods or instruments employed vary. Results One hundred fifty-one patients were included, of whom 75 (50%) had negative KUB and positive NCCT results for ureteric stones based on the blinded review. Lower ureteral calculi were found to be the most common location in both KUB (n = 49, 65%) and NCCT images (n = 81, 54%). The median stone diameters of KUB and NCCT were 5 (3-8) mm and 6 (4-9) mm, respectively. Hounsfield unit densities of more than 630 were found in 86 (57%) patients, and radiopaque stones were found in 76 (50%) patients. There was moderate and significant concordance (Cohen's kappa = 0.520) between NCCT and KUB regarding stone location (P < 0.01). There was a strong concordance (Cohen's kappa = 0.804) between NCCT and KUB in detecting ureteric stone maximum diameter (P < 0.01). Stone density was weakly correlated between KUB and NCCT (Cohen's kappa = 0.254) (P = 0.001). Thirty-four cases (45%) of negative KUB results required surgical intervention (SI). Sepsis (n = 5, 15%) and acute kidney injury (n = 23, 68%) were the main indications for SI in negative KUB and positive NCCT ureteric stones. Conclusions KUB radiography should not be used as a triage tool in AUC due to potentially harmful outcomes. However, KUB radiography can be reliably used during follow-up, as there is a strong correlation between KUB radiography and NCCT for KUB-detectable ureteric stones.
<b><i>Objective:</i></b> We sought to use artificial intelligence (AI) to develop and test calculators to predict spontaneous stone passage (SSP) using radiographical and clinical data. <b><i>Methods:</i></b> Consecutive patients with solitary ureteral stones โค10 mm on CT were prospectively enrolled and managed according to American Urological Association guidelines. The first 70% of patients were placed in the "training group" and used to develop the calculators. The latter 30% were enrolled in the "testing group" to externally validate the calculators. Exclusion criteria included contraindication to trial of SSP, ureteral stent, and anatomical anomaly. Demographic, clinical, and radiographical data were obtained and fed into machine learning (ML) platforms. SSP was defined as passage of stone without intervention. Calculators were derived from data using multivariate logistic regression. Discrimination, calibration, and clinical utility/net benefit of the developed models were assessed in the validation cohort. Receiver operating characteristic curves were constructed to measure their discriminative ability. <b><i>Results:</i></b> Fifty-one percent of 131 "training" patients spontaneously passed their stones. Passed stones were significantly closer to the bladder (8.6 <i>vs</i> 11.8 cm, p = 0.01) and smaller in length, width, and height. Two ML calculators were developed, one supervised machine learning (SML) and the other unsupervised machine learning (USML), and compared to an existing tool Multi-centre Cohort Study Evaluating the role of Inflammatory Markers In Patients Presenting with Acute Ureteric Colic (MIMIC). The SML calculator included maximum stone width (MSW), ureteral diameter above the stone (UDA), and distance from ureterovesical junction to bottom of stone and had an area under the curve (AUC) of 0.737 upon external validation of 58 "test" patients. Parameters selected by USML included MSW, UDA, and use of an anticholinergic, and it had an AUC of 0.706. The MIMIC calculator's AUC was 0.588 (0.489-0.686). <b><i>Conclusion:</i></b> We used AI to develop calculators that outperformed an existing tool and can help providers and patients make a better-informed decision for the treatment of ureteral stones.
Exploring the efficacy of an artificial intelligence (AI) model derived from the analysis of computed tomography (CT) images to precisely forecast the therapeutic outcomes of singular-session extracorporeal shock wave lithotripsy (ESWL) in the management of ureteral stones.
A total of 317 patients diagnosed clinically with ureteral stones were included in this investigation. Unenhanced CT was administered to the participants within the initial fortnight preceding the inaugural ESWL. The internal cohort consisted of 250 individuals from a local healthcare facility, whereas the external cohort comprised 67 participants from another local medical institution. The proposed framework comprises three main components: an automated semantic segmentation model developed using 3D U-Net, a feature extractor that integrates radiomics and autoencoder techniques, and an ESWL efficacy prediction model trained with various machine learning algorithms. All participants underwent thorough postoperative follow-up examinations 4 weeks hence. The efficacy of ESWL was defined by the absence of stones or residual fragments measuring โค2ย mm in KUB X-ray assessments. Model stability and generalizability were judiciously validated through a fivefold cross-validation approach and a multicenter external test strategy. Moreover, Shapley Additive Explanations (SHAP) values for individual features were computed to elucidate the nuanced contributions of each feature to the model's decision-making process.
The semantic segmentation model the authors constructed exhibited an average Dice coefficient of 0.88ยฑ0.08 on the external testing set. ESWL classifiers built using Support Vector Machine (SVM), Random Forest (RF), XGBoost (XB), and CatBoost (CB) achieved AUROC values of 0.78, 0.84, 0.85, and 0.90, respectively, on the internal validation set. For the external testing set, SVM, RF, XB, and CB predicted ESWL with AUROC values of 0.68, 0.79, 0.80, and 0.83, respectively, with the last one being the optimal algorithm. The radiomics features and auto-encoder features made significant contributions to the decision-making process of the classification model.
This investigation unmistakably underscores the remarkable predictive prowess exhibited by a scrupulously crafted AI model using CT images to precisely anticipate the therapeutic results of a singular session of ESWL for ureteral stones.
To present state of the art on the management of urinary stones from a panel of globally recognized urolithiasis experts who met during the Experts in Stone Disease Congress in Valencia in January 2024. Options of treatment: The surgical treatment modalities of renal and ureteral stones are well defined by the guidelines of international societies, although for some index cases more alternative options are possible. For 1.5 cm renal stones, both m-PCNL and RIRS have proven to be valid treatment alternatives with comparable stone-free rates. The m-PCNL has proven to be more cost effective and requires a shorter operative time, while the RIRS has demonstrated lower morbidity in terms of blood loss and shorter recovery times. SWL has proven to be less effective at least for lower calyceal stones but has the highest safety profile. For a 6mm obstructing stone of the pelviureteric junction (PUJ) stone, SWL should be the first choice for a stone less than 1 cm, due to less invasiveness and lower risk of complications although it has a lower stone free-rate. RIRS has advantages in certain conditions such as anticoagulant treatment, obesity, or body deformity. Technical issues of the surgical procedures for stone removal: In patients receiving antithrombotic therapy, SWL, PCN and open surgery are at elevated risk of hemorrhage or perinephric hematoma. URS, is associated with less morbidity in these cases. An individualized combined evaluation of risks of bleeding and thromboembolism should determine the perioperative thromboprophylactic strategy. Pre-interventional urine culture and antibiotic therapy are mandatory although UTI treatment is becoming more challenging due to increasing resistance to routinely applied antibiotics. The use of an intrarenal urine culture and stone culture is recommended to adapt antibiotic therapy in case of postoperative infectious complications. Measurements of temperature and pressure during RIRS are vital for ensuring patient safety and optimizing surgical outcomes although techniques of measurements and methods for data analysis are still to be refined. Ureteral stents were improved by the development of new biomaterials, new coatings, and new stent designs. Topics of current research are the development of drug eluting and bioresorbable stents. Complications of endoscopic treatment: PCNL is considered the most invasive surgical option. Fever and sepsis were observed in 11 and 0.5% and need for transfusion and embolization for bleeding in 7 and 0.4%. Major complications, as colonic, splenic, liver, gall bladder and bowel injuries are quite rare but are associated with significant morbidity. Ureteroscopy causes less complications, although some of them can be severe. They depend on high pressure in the urinary tract (sepsis or renal bleeding) or application of excessive force to the urinary tract (ureteral avulsion or stricture). Diagnostic work up: ย Genetic testing consents the diagnosis of monogenetic conditions causing stones. It should be carried out in children and in selected adults. In adults, monogenetic diseases can be diagnosed by systematic genetic testing in no more than 4%, when cystinuria, APRT deficiency, and xanthinuria are excluded. A reliable stone analysis by infrared spectroscopy or X-ray diffraction is mandatory and should be associated to examination of the stone under a stereomicroscope. The analysis of digital images of stones by deep convolutional neural networks in dry laboratory or during endoscopic examination could allow the classification of stones based on their color and texture. Scanning electron microscopy (SEM) in association with energy dispersive spectrometry (EDS) is another fundamental research tool for the study of kidney stones. The combination of metagenomic analysis using Next Generation Sequencing (NGS) techniques and the enhanced quantitative urine culture (EQUC) protocol can be used to evaluate the urobiome of renal stone formers. Twenty-four hour urine analysis has a place during patient evaluation together with repeated measurements of urinary pH with a digital pH meter. Urinary supersaturation is the most comprehensive physicochemical risk factor employed in urolithiasis research. Urinary macromolecules can act as both promoters or inhibitors of stone formation depending on the chemical composition of urine in which they are operating. At the moment, there are no clinical applications of macromolecules in stone management or prophylaxis. Patients should be evaluated for the association with systemic pathologies. PROPHYLAXIS: Personalized medicine and public health interventions are complementary to prevent stone recurrence. Personalized medicine addresses a small part of stone patients with a high risk of recurrence and systemic complications requiring specific dietary and pharmacological treatment to prevent stone recurrence and complications of associated systemic diseases. The more numerous subjects who form one or a few stones during their entire lifespan should be treated by modifications of diet and lifestyle. Primary prevention by public health interventions is advisable to reduce prevalence of stones in the general population. Renal stone formers at "high-risk" for recurrence need early diagnosis to start specific treatment. Stone analysis allows the identification of most "high-risk" patients forming non-calcium stones: infection stones (struvite), uric acid and urates, cystine and other rare stones (dihydroxyadenine, xanthine). Patients at "high-risk" forming calcium stones require a more difficult diagnosis by clinical and laboratory evaluation. Particularly, patients with cystinuria and primary hyperoxaluria should be actively searched. FUTURE RESEARCH: Application of Artificial Intelligence are promising for automated identification of ureteral stones on CT imaging, prediction of stone composition and 24-hour urinary risk factors by demographics and clinical parameters, assessment of stone composition by evaluation of endoscopic images and prediction of outcomes of stone treatments. The synergy between urologists, nephrologists, and scientists in basic kidney stone research will enhance the depth and breadth of investigations, leading to a more comprehensive understanding of kidney stone formation.
A kidney stone is a solid formation that can lead to kidney failure, severe pain, and reduced quality of life from urinary system blockages. While medical experts can interpret kidney-ureter-bladder (KUB) X-ray images, specific images pose challenges for human detection, requiring significant analysis time. Consequently, developing a detection system becomes crucial for accurately classifying KUB X-ray images. This article applies a transfer learning (TL) model with a pre-trained VGG16 empowered with explainable artificial intelligence (XAI) to establish a system that takes KUB X-ray images and accurately categorizes them as kidney stones or normal cases. The findings demonstrate that the model achieves a testing accuracy of 97.41% in identifying kidney stones or normal KUB X-rays in the dataset used. VGG16 model delivers highly accurate predictions but lacks fairness and explainability in their decision-making process. This study incorporates the Layer-Wise Relevance Propagation (LRP) technique, an explainable artificial intelligence (XAI) technique, to enhance the transparency and effectiveness of the model to address this concern. The XAI technique, specifically LRP, increases the model's fairness and transparency, facilitating human comprehension of the predictions. Consequently, XAI can play an important role in assisting doctors with the accurate identification of kidney stones, thereby facilitating the execution of effective treatment strategies.
In recent years, machine learning (ML) and deep learning (DL) have been the leading approaches to solving various challenges, such as disease predictions, drug discovery, medical image analysis, etc., in intelligent healthcare applications. Further, given the current progress in the fields of ML and DL, there exists the promising potential for both to provide support in the realm of healthcare. This study offered an exhaustive survey on ML and DL for the healthcare system, concentrating on vital state of the art features, integration benefits, applications, prospects and future guidelines. To conduct the research, we found the most prominent journal and conference databases using distinct keywords to discover scholarly consequences. First, we furnished the most current along with cutting-edge progress in ML-DL-based analysis in smart healthcare in a compendious manner. Next, we integrated the advancement of various services for ML and DL, including ML-healthcare, DL-healthcare, and ML-DL-healthcare. We then offered ML and DL-based applications in the healthcare industry. Eventually, we emphasized the research disputes and recommendations for further studies based on our observations.
We recommend that the guidelines should undergo regular updates based on recently published material, and while these guidelines provide a framework, treatment plans should still be personalised, respecting patient preferences, surgical expertise, and various other individual factors, to offer the best outcome for kidney stone patients.
Kidney stones and urolithiasis are kidney diseases that have a significant impact on health and well-being, and their incidence is increasing annually owing to factors such as age, sex, ethnicity, and geographical location. Accurate identification and volume measurement of kidney stones are critical for determining the appropriate surgical approach, as timely and precise treatment is essential to prevent complications and ensure successful outcomes. Larger stones often require more invasive procedures, and precise volume measurements are essential for effective surgical planning and patient outcomes. This study aimed to compare the ability of artificial intelligence (AI) to detect and measure kidney stone volume via CT-KUB images. CT KUB imaging data were analyzed to determine the effectiveness of AI in identifying the volume of kidney stones. The results were compared with measurements taken by radiologists. Compared with radiologists, the AI had greater accuracy, efficiency, and consistency in measuring kidney stone volume. The AI calculates the volume of kidney stones with an average difference of 80% compared with the volumes calculated by radiologists, highlighting a significant discrepancy that is critical for accurate surgical planning. The results suggest that artificial intelligence (AI) outperforms radiologists' manual calculations in measuring kidney stone volume. By integrating AI with kidney stone detection and treatment, there is potential for greater diagnostic precision and treatment effectiveness, which could ultimately improve patient outcomes.
Background and
We attempted to determine the optimal radiation dose to maintain image quality using a deep learning application in a physical human phantom.
Three 5 ร 5 ร 5 mm3 uric acid stones were placed in a physical human phantom in various locations. Three tube voltages (120, 100, and 80 kV) and four current-time products (100, 70, 30, and 15 mAs) were implemented in 12 scans. Each scan was reconstructed with filtered back projection (FBP), statistical iterative reconstruction (IR, iDose), and knowledge-based iterative model reconstruction (IMR). By applying deep learning to each image, we took 12 more scans. Objective image assessments were calculated using the standard deviation of the Hounsfield unit (HU). Subjective image assessments were performed by one radiologist and one urologist. Two radiologists assessed the subjective assessment and found the stone under the absence of information. We used this data to calculate the diagnostic accuracy.
Objective image noise was decreased after applying a deep learning tool in all images of FBP, iDose, and IMR. There was no statistical difference between iDose and deep learning-applied FBP images (10.1 ยฑ 11.9, 9.5 ยฑ 18.5 HU, p = 0.583, respectively). At a 100 kV-30 mAs setting, deep learning-applied FBP obtained a similar objective noise in approximately one third of the radiation doses compared to FBP. In radiation doses with settings lower than 100 kV-30 mAs, the subject image assessment (image quality, confidence level, and noise) showed deteriorated scores. Diagnostic accuracy was increased when the deep learning setting was lower than 100 kV-30 mAs, except for at 80 kV-15 mAs.
At the setting of 100 kV-30 mAs or higher, deep learning-applied FBP did not differ in image quality compared to IR. At the setting of 100 kV-30 mAs, the radiation dose can decrease by about one third while maintaining objective noise.
This study aimed to compare the image quality and diagnostic accuracy of deep-learning-based image denoising reconstructions (DLIDs) to established iterative reconstructed algorithms in low-dose computed tomography (LDCT) of patients with suspected urolithiasis. LDCTs (CTDIvol, 2 mGy) of 76 patients (age: 40.3 ยฑ 5.2 years, M/W: 51/25) with suspected urolithiasis were retrospectively included. Filtered-back projection (FBP), hybrid iterative and model-based iterative reconstruction (HIR/MBIR, respectively) were reconstructed. FBP images were processed using a Food and Drug Administration (FDA)-approved DLID. ROIs were placed in renal parenchyma, fat, muscle and urinary bladder. Signal- and contrast-to-noise ratios (SNR/CNR, respectively) were calculated. Two radiologists evaluated image quality on five-point Likert scales and urinary stones. The results showed a progressive decrease in image noise from FBP, HIR and DLID to MBIR with significant differences between each method (p < 0.05). SNR and CNR were comparable between MBIR and DLID, while it was significantly lower in HIR followed by FBP (e.g., SNR: 1.5 ยฑ 0.3; 1.4 ยฑ 0.4; 1.0 ยฑ 0.3; 0.7 ยฑ 0.2, p < 0.05). Subjective analysis confirmed best image quality in MBIR, followed by DLID and HIR, both being superior to FBP (p < 0.05). Diagnostic accuracy for urinary stone detection was best using MBIR (0.94), lowest using FBP (0.84) and comparable between DLID (0.90) and HIR (0.90). Stone size measurements were consistent between all reconstructions and showed excellent correlation (r2 = 0.958-0.975). In conclusion, MBIR yielded the highest image quality and diagnostic accuracy, with DLID producing better results than HIR and FBP in image quality and matching HIR in diagnostic precision.
The adoption of artificial intelligence (AI) algorithms is rapidly increasing in healthcare. Such algorithms may be shaped by various factors such as social determinants of health that can influence health outcomes. While AI algorithms have been proposed as a tool to expand the reach of quality healthcare to underserved communities and improve health equity, recent literature has raised concerns about the propagation of biases and healthcare disparities through implementation of these algorithms. Thus, it is critical to understand the sources of bias inherent in AI-based algorithms. This review aims to highlight the potential sources of bias within each step of developing AI algorithms in healthcare, starting from framing the problem, data collection, preprocessing, development, and validation, as well as their full implementation. For each of these steps, we also discuss strategies to mitigate the bias and disparities. A checklist was developed with recommendations for reducing bias during the development and implementation stages. It is important for developers and users of AI-based algorithms to keep these important considerations in mind to advance health equity for all populations.
Kidney-ureter-bladder (KUB) imaging is used as a frontline investigation for patients with suspected renal stones. In this study, we designed a computer-aided diagnostic system for KUB imaging to assist clinicians in accurately diagnosing urinary tract stones. The image dataset used for training and testing the model comprised 485 images provided by Kaohsiung Chang Gung Memorial Hospital. The proposed system was divided into two subsystems, 1 and 2. Subsystem 1 used Inception-ResNetV2 to train a deep learning model on preprocessed KUB images to verify the improvement in diagnostic accuracy with image preprocessing. Subsystem 2 trained an image segmentation model using the ResNet hybrid, U-net, to accurately identify the contours of renal stones. The performance was evaluated using a confusion matrix for the classification model. We conclude that the model can assist clinicians in accurately diagnosing renal stones via KUB imaging. Therefore, the proposed system can assist doctors in diagnosis, reduce patients' waiting time for CT scans, and minimize the radiation dose absorbed by the body.
PURPOSE OF REVIEW: Radiological imaging techniques and applications are constantly advancing. This review will examine modern imaging techniques in the diagnosis of urolithiasis and applications for surgical planning. RECENT
The diagnosis of urolithiasis may be done via plain film X-ray, ultrasound (US), or contrast tomography (CT) scan. US should be applied in the workup of flank pain in emergency rooms and may reduce unnecessary radiation exposure. Low dose and ultra-low-dose CT remain the diagnostic standard for most populations but remain underutilized. Single and dual-energy CT provide three-dimensional imaging that can predict stone-specific parameters that help clinicians predict stone passage likelihood, identify ideal management techniques, and possibly reduce complications. Machine learning has been increasingly applied to 3-D imaging to support clinicians in these prognostications and treatment selection. SUMMARY: The diagnosis and management of urolithiasis are increasingly personalized. Patient and stone characteristics will support clinicians in treatment decision, surgical planning, and counseling.
To determine the diagnostic accuracy and image quality of ultra-low-dose computed tomography (ULDCT) with deep learning reconstruction (DLR) to evaluate patients with suspected urolithiasis, compared with ULDCT with hybrid iterative reconstruction (HIR) by using low-dose CT (LDCT) with HIR as the reference standard.
Patients with suspected urolithiasis were prospectively enrolled and underwent abdominopelvic LDCT, followed by ULDCT if any urinary stone was observed. Radiation exposure, stone characteristics, image noise, signal-to-noise ratio (SNR), and subjective image quality on a 5-point Likert scale were evaluated and compared.
The average effective radiation dose of ULDCT was significantly lower than that of LDCT (1.28 ยฑ 0.34 vs. 5.49 ยฑ 1.00 mSv, p < 0.001). According to the reference standard (LDCT-HIR), 148 urinary stones were observed in 85.0% (51/60) of patients. ULDCT-DLR detected 143 stones with a rate of 96.6%, and ULDCT-HIR detected 142 stones with a rate of 95.9%. The urinary stones that were not observed with ULDCT-DLR or ULDCT-HIR were renal calculi smaller than 3 mm. There were no significant differences in the detection of clinically significant calculi (โฅ 3 mm) or stone size estimation among ULDCT-DLR, ULDCT-HIR, and LDCT-HIR. The image quality of ULDCT-DLR was better than that of ULDCT-HIR and LDCT-HIR with lower image noise, higher SNR, and higher average subjective score.
ULDCT-DLR performed comparably to LDCT-HIR in urinary stone detection and size estimation with better image quality and decreased radiation exposure. ULDCT-DLR may have potential to be considered the first-line choice to evaluate urolithiasis in practice. KEY POINTS: โข Ultra-low-dose computed tomography (ULDCT) has been investigated for diagnosis of urolithiasis, but stone evaluation may be adversely impacted by compromised image quality. โข This study evaluated the value of novel deep learning reconstruction (DLR) at ULDCT by comparing the stone evaluation and image quality of ULDCT-DLR to the reference standard of low-dose CT (LDCT) with hybrid iterative reconstruction (HIR). โข ULDCT-DLR performed comparably to LDCT-HIR in urinary stone detection and size estimation with better image quality and reduced radiation exposure.
Our deep-learning-based system showed improvements over a previously developed system that did not use deep learning, with even higher performance on an external validation set.
The series of recommendations regarding RIRS, along with the related commentary and supporting documentation, offered here should help provide safe and effective performance of RIRS.
Kidney-ureter-bladder (KUB) imaging is a radiological examination with a low cost, low radiation, and convenience. Although emergency room clinicians can arrange KUB images easily as a first-line examination for patients with suspicious urolithiasis, interpreting the KUB images correctly is difficult for inexperienced clinicians. Obtaining a formal radiology report immediately after a KUB imaging examination can also be challenging. Recently, artificial-intelligence-based computer-aided diagnosis (CAD) systems have been developed to help clinicians who are not experts make correct diagnoses for further treatment more effectively. Therefore, in this study, we proposed a CAD system for KUB imaging based on a deep learning model designed to help first-line emergency room clinicians diagnose urolithiasis accurately. A total of 355 KUB images were retrospectively collected from 104 patients who were diagnosed with urolithiasis at Kaohsiung Chang Gung Memorial Hospital. Then, we trained a deep learning model with a ResNet architecture to classify KUB images in terms of the presence or absence of kidney stones with this dataset of pre-processed images. Finally, we tuned the parameters and tested the model experimentally. The results show that the accuracy, sensitivity, specificity, and F1-measure of the model were 0.977, 0.953, 1, and 0.976 on the validation set and 0.982, 0.964, 1, and 0.982 on the testing set, respectively. Moreover, the results demonstrate that the proposed model performed well compared to the existing CNN-based methods and was able to detect urolithiasis in KUB images successfully. We expect the proposed approach to help emergency room clinicians make accurate diagnoses and reduce unnecessary radiation exposure from computed tomography (CT) scans, along with the associated medical costs.
The use of deep learning algorithms for the detection of kidney stones is reliable and effective. Additionally, these algorithms can reduce the reporting time and cost of CT-dependent urolithiasis detection, leading to early diagnosis and management.
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.
We collected plain X-ray images of 1017 patients with a radio-opaque upper urinary tract stone. X-ray images (nโ=โ827 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.
Using deep learning, we developed a CAD model that needed 110ย 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.
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.
Urolithiasis is a global disease with a high incidence and recurrence rate, and stone composition is closely related to the choice of treatment and preventive measures. Calcium oxalate monohydrate (COM) is the most common in clinical practice, which is hard and difficult to fragment. Preoperative identification of its components and selection of effective surgical methods can reduce the risk of patients having a second operation. Methods that can be used for stone composition analysis include infrared spectroscopy, X-ray diffraction, and polarized light microscopy, but they are all performed on stone specimens in vitro after surgery. This study aimed to design and develop an artificial intelligence (AI) model based on unenhanced computed tomography (CT) images of the urinary tract, and to investigate the predictive ability of the model for COM stones in vivo preoperatively, so as to provide surgeons with more accurate diagnostic information.
Preoperative unenhanced CT images of patients with urinary calculi whose components were determined by infrared spectroscopy in a single center were retrospectively analyzed, including 337 cases of COM stones and 170 of non-COM stones. All images were manually segmented and the image features were extracted, and randomly divided into the training and testing sets in a ratio of 7:3. The least absolute shrinkage and selection operation algorithm (LASSO) was used to construct the AI model, and classification of the training and testing sets was carried out.
A total of 1,218 radiomics imaging features were extracted, and 8 features with non-zero coefficients were finally obtained. The sensitivity, specificity and accuracy of the AI model were 90.5%, 84.3% and 88.5% for the training set, and 90.1%, 84.3% and 88.3% for the testing set. The area under the curve was 0.935 for the training set and 0.933 for the testing set.
The AI model based on unenhanced CT images of the urinary tract can predict COM and non-COM stones in vivo preoperatively, and the model has high sensitivity, specificity and accuracy.
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. 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.