Kidney Stone Detection Research Articles

Evaluating the effectiveness of AI-powered UrologiQ's in accurately measuring kidney stone volume in urolithiasis 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.

A Review on Kidney Stone Detection using ML and DL Techniques

Kidney stone detection is a critical healthcare challenge, as timely and accurate diagnosis can prevent complications. The motivation behind this review is the increasing prevalence of kidney stones and the need for more effective, non-invasive detection methods. Machine learning (ML) and deep learning (DL) techniques offer promising solutions, leveraging medical imaging data to enhance diagnostic accuracy. However, limitations such as high computational cost and reliance on large datasets hinder their full potential. The aim of this review is to analyze the latest advancements in kidney stone detection using ML and DL techniques. The objective is to compare existing methodologies, highlight their strengths and weaknesses, and suggest future research directions, particularly in integrating transfer learning and fine-tuning techniques to enhance performance.

Kidney Stone Detection based on Improved YOLOv7 with Attention Module and Super Resolution Techniques Under Limited Training Samples

Kidney Stone Detection based on Improved YOLOv7 with Attention Module and Super Resolution Techniques Under Limited Training Samples

Urinary stone assessment in a single-phase may replace the unenhanced and multiphase computed tomography protocol in painless visible haematuria.

Painless visible haematuria (VH) necessitates a computed tomography (CT) usually consisting of one unenhanced and two to three contrast enhanced acquisitions to detect urinary tract stones and malignancy. Recently, we demonstrated that a single nephrographic phase (NP) CT sufficed in detecting malignancy in patients with painless VH. Now, we aim to evaluate the diagnostic performance of single NP CT in stone detection and size measurements in the same cohort. "A Prospective Trial for Examining Haematuria using Computed Tomography" (PROTEHCT) was a single-center prospective diagnostic study in patients with painless VH between September 2019 and June 2021. All underwent four-phase CT (reference standard) from which a single NP CT (experimental) was extracted. Two randomised readers independently assessed the experimental CT for urinary stones and size. Statistical analysis included diagnostic accuracies and inter-reader agreement (kappa) of experimental CT, and size correlation (Spearman's ρ) between experimental CT and reference standard. In 308 included patients (median age: 68 years, 250 males), urinary stones (median size 5 mm) were diagnosed in 21%. The per-patient experimental CT sensitivity was 86% (97% for stones ≥ 5 mm), specificity was 98% and accuracy was 96%. The experimental CT sensitivity for detecting kidney stones was 78% (89% for stones ≥ 5 mm), and 100% for bladder and ureteral stones. No missed stone required active treatment. The inter-reader agreement was almost perfect (96%, k = 0.85). The correlation in stone size was very strong (ρ = 0.91). Conclusions: A single NP CT is sufficient in detecting and measuring urinary stones in patients with painless VH.

The impact of deep learning image reconstruction of spectral CTU virtual non contrast images for patients with renal stones

PurposeTo compare image quality and detection accuracy of renal stones between deep learning image reconstruction (DLIR) and Adaptive Statistical Iterative Reconstruction-Veo (ASIR-V) reconstructed virtual non-contrast (VNC) images and true non-contrast (TNC) images in spectral CT Urography (CTU). MethodsA retrospective analysis was conducted on images of 70 patients who underwent abdominal-pelvic CTU in TNC phase using non-contrast scan and contrast-enhanced corticomedullary phase (CP) and excretory phase (EP) using spectral scan. The TNC scan was reconstructed using ASIR-V70 % (TNC-AR70), contrast-enhanced scans were reconstructed using AR70, DLIR medium-level (DM), and high-level (DH) to obtain CP-VNC-AR70/DM/DH and EP-VNC-AR70/DM/DH image groups, respectively. CT value, image quality and kidney stones quantification accuracy were measured and compared among groups. The subjective evaluation was independently assessed by two senior radiologists using the 5-point Likert scale for image quality and lesion visibility. ResultsDH images were superior to AR70 and DM images in objective image quality evaluation. There was no statistical difference in the liver and spleen (both P > 0.05), or within 6HU in renal and fat in CT value between VNC and TNC images. EP-VNC-DH had the lowest image noise, highest SNR, and CNR, and VNC-AR70 images had better noise and SNR performance than TNC-AR70 images (all p < 0.05). EP-VNC-DH had the highest subjective image quality, and CP-VNC-DH performed the best in lesion visibility. In stone CT value and volume measurements, there was no statistical difference between VNC and TNC (P > 0.05). ConclusionThe DLIR-reconstructed VNC images in CTU provide better image quality than the ASIR-V reconstructed TNC images and similar quantification accuracy for kidney stones for potential dose savings.The study highlights that deep learning image reconstruction (DLIR)-reconstructed virtual non-contrast (VNC) images in spectral CT Urography (CTU) offer improved image quality compared to traditional true non-contrast (TNC) images, while maintaining similar accuracy in kidney stone detection, suggesting potential dose savings in clinical practice.

Utilizing Deep Learning to Detect Kidney Stones from CT Scan Data

Utilizing Deep Learning to Detect Kidney Stones from CT Scan Data

Real-Time Object Detection in Medical Imaging Using YOLO Models for Kidney Stone Detection

Real-Time Object Detection in Medical Imaging Using YOLO Models for Kidney Stone Detection

A Novel Deep Learning–based Artificial Intelligence System for Interpreting Urolithiasis in Computed Tomography

Background and objectiveOur aim was to develop an artificial intelligence (AI) system for detection of urolithiasis in computed tomography (CT) images using advanced deep learning capable of real-time calculation of stone parameters such as volume and density, which are essential for treatment decisions. The performance of the system was compared to that of urologists in emergency room (ER) scenarios. MethodsAxial CT images for patients who underwent stone surgery between August 2022 and July 2023 comprised the data set, which was divided into 70% for training, 10% for internal validation, and 20% for testing. Two urologists and an AI specialist annotated stones using Labelimg for ground-truth data. The YOLOv4 architecture was used for training, with acceleration via an RTX 4900 graphics processing unit (GPU). External validation was performed using CT images for 100 patients with suspected urolithiasis. Key findings and limitationsThe AI system was trained on 39 433 CT images, of which 9.1% were positive. The system achieved accuracy of 95%, peaking with a 1:2 positive-to-negative sample ratio. In a validation set of 5736 images (482 positive), accuracy remained at 95%. Misses (2.6%) were mainly irregular stones. False positives (3.4%) were often due to artifacts or calcifications. External validation using 100 CT images from the ER revealed accuracy of 94%; cases that were missed were mostly ureterovesical junction stones, which were not included in the training set. The AI system surpassed human specialists in speed, analyzing 150 CT images in 13 s, versus 38.6 s for evaluation by urologists and 23 h for formal reading. The AI system calculated stone volume in 0.2 s, versus 77 s for calculation by urologists. Conclusions and clinical implicationsOur AI system, which uses advanced deep learning, assists in diagnosing urolithiasis with 94% accuracy in real clinical settings and has potential for rapid diagnosis using standard consumer-grade GPUs. Patient summaryWe developed a new AI (artificial intelligence) system that can quickly and accurately detect kidney stones in CT (computed tomography) scans. Testing showed that this system is highly effective, with accuracy of 94% for real cases in the emergency department. It is much faster than traditional methods and provides rapid and reliable results to help doctors in making better treatment decisions for their patients.

Deployment of Web-Based YOLO for CT Scan Kidney Stone Detection

This research aims to develop a kidney stone object detection system using machine learning techniques like YOLO and object detection, integrated into a Flask-based web interface to support early diagnosis by medical professionals. The trained model demonstrates strong pattern learning capabilities. Evaluation of the public dataset model reveals an average mean Average Precision (mAP) of 0.9698 for 'kidney stone' labels. This detection model exhibits high performance with an accuracy rate of 96.33%, precision of 96.98%, recall of 99.23%, and an F1-score of 98.1%. Clinical data evaluation shows that the YOLOv5-based detection system performs exceptionally well, with an average mAP of 0.9571, accuracy of 93.06%, precision of 95.71%, recall of 97.1%, and F1-score of 96.49%, indicating the model's capability to detect kidney stones with high precision and accuracy. Thus, both the evaluation on the public dataset and clinical dataset performance support accurate diagnosis processes and further treatment planning. Moreover, this research advances to the stage where the detection model can be directly utilized through implementation via Flask web deployment.

Using Machine Learning for Non-Invasive Detection of Kidney Stones Based on Laboratory Test Results: A Case Study from a Saudi Arabian Hospital.

Kidney stone disease is a widespread urological disorder affecting millions globally. Timely diagnosis is crucial to avoid severe complications. Traditionally, renal stones are detected using computed tomography (CT), which, despite its effectiveness, is costly, resource-intensive, exposes patients to unnecessary radiation, and often results in delays due to radiology report wait times. This study presents a novel approach leveraging machine learning to detect renal stones early using routine laboratory test results. We utilized an extensive dataset comprising 2156 patient records from a Saudi Arabian hospital, featuring 15 attributes with challenges such as missing data and class imbalance. We evaluated various machine learning algorithms and imputation methods, including single and multiple imputations, as well as oversampling and undersampling techniques. Our results demonstrate that ensemble tree-based classifiers, specifically random forest (RF) and extra tree classifiers (ETree), outperform others with remarkable accuracy rates of 99%, recall rates of 98%, and F1 scores of 99% for RF, and 92% for ETree. This study underscores the potential of non-invasive, cost-effective laboratory tests for renal stone detection, promoting prompt and improved medical support.

Integrative approach for efficient detection of kidney stones based on improved deep neural network architecture

In today's digital world, with growing population and increasing pollution, unhealthy lifestyle habits like irregular eating, junk food consumption, and lack of exercise are becoming more common, leading to various health problems, including kidney issues. These factors directly affect human kidney health. To address this, we require early detection techniques that rely on text data. Text data contains detailed information about a patient's medical history, symptoms, test results, and treatment plans, giving a complete picture of kidney health and enabling timely intervention. In this research paper, we proposed a range of sophisticated models, such as Gradient Boosting Classifier, Light GBM, CatBoost, Support Vector Classifier (SVC), Random Boost, Logistic Regression, XGBoost, Deep Neural Network (DNN), and an Improved DNN. The Improved DNN demonstrated exceptional performance, with an accuracy of 90 %, precision of 89 %, recall of 90 %, and an F1-Score of 89.5 %. By combining traditional machine learning and deep neural networks, this integrative approach enables the identification of intricate patterns in datasets. The model's data-driven processes consistently update internal parameters, guaranteeing flexibility in response to evolving healthcare settings. This research represents a notable advancement in the progress of creating a more detailed and individualised ability to diagnose kidney stones, which could potentially lead to better clinical results and patient treatment.

Bedside Sonography in the Assessment of Suspected Nephrolitiasis

Background: Nephrolithiasis, commonly referred to as kidney stones, is a prevalent urological condition frequently encountered in emergency settings. Rapid and accurate diagnosis is critical for effective management. Bedside sonography has emerged as a valuable diagnostic tool for assessing renal colic and detecting kidney stones due to its non-invasive nature and immediate availability. Objective: To evaluate the diagnostic accuracy and practicality of bedside sonography in suspected cases of nephrolithiasis, particularly in emergency department settings, and to assess its impact on patient management. Methods: A cross-sectional study was conducted at Mahaban Medical and Research Hospital, Swabi, KP, Pakistan, including 72 patients with suspected nephrolithiasis. Bedside sonography was performed using a Toshiba prime ultrasound machine with convex (3.0-5.0 MHz) and linear (7.0-14.0 MHz) transducers. Clinical data, including patient history and symptomatology, were collected and analyzed using SPSS version 25.0. The study focused on identifying the presence, location, and size of kidney stones and correlating these findings with clinical outcomes. Results: Among the 72 participants, 42 (58.3%) had a history of kidney stones. Bedside sonography revealed stone locations as follows: bilateral in 5 (6.9%), left kidney in 27 (37.5%), right kidney in 36 (50.0%), and no stones in 4 (5.6%). The high prevalence of kidney stones, particularly in the right kidney, highlights the importance of BUS in initial evaluations. Conclusion: Bedside sonography is an effective diagnostic tool for nephrolithiasis, providing immediate and detailed information without patient discomfort. Its implementation in emergency settings can enhance patient management, allowing for timely diagnosis and treatment. Further research is warranted to explore the broader applications and long-term outcomes associated with BUS.

A Comprehensive Study of Deep Learning Methods for Kidney Tumor, Cyst, and Stone Diagnostics and Detection Using CT Images

A Comprehensive Study of Deep Learning Methods for Kidney Tumor, Cyst, and Stone Diagnostics and Detection Using CT Images

Stone decision engine accurately predicts stone removal and treatment complications for shock wave lithotripsy and laser ureterorenoscopy patients.

Kidney stones form when mineral salts crystallize in the urinary tract. While most stones exit the body in the urine stream, some can block the ureteropelvic junction or ureters, leading to severe lower back pain, blood in the urine, vomiting, and painful urination. Imaging technologies, such as X-rays or ureterorenoscopy (URS), are typically used to detect kidney stones. Subsequently, these stones are fragmented into smaller pieces using shock wave lithotripsy (SWL) or laser URS. Both treatments yield subtly different patient outcomes. To predict successful stone removal and complication outcomes, Artificial Neural Network models were trained on 15,126 SWL and 2,116 URS patient records. These records include patient metrics like Body Mass Index and age, as well as treatment outcomes obtained using various medical instruments and healthcare professionals. Due to the low number of outcome failures in the data (e.g., treatment complications), Nearest Neighbor and Synthetic Minority Oversampling Technique (SMOTE) models were implemented to improve prediction accuracies. To reduce noise in the predictions, ensemble modeling was employed. The average prediction accuracies based on Confusion Matrices for SWL stone removal and treatment complications were 84.8% and 95.0%, respectively, while those for URS were 89.0% and 92.2%, respectively. The average prediction accuracies for SWL based on Area-Under-the-Curve were 74.7% and 62.9%, respectively, while those for URS were 77.2% and 78.9%, respectively. Taken together, the approach yielded moderate to high accurate predictions, regardless of treatment or outcome. These models were incorporated into a Stone Decision Engine web application (http://peteranoble.com/webapps.html) that suggests the best interventions to healthcare providers based on individual patient metrics.

B-Mode Sonography Versus Color Doppler Twinkling Artifact in the Diagnosis of Nephrolithiasis

Background: Nephrolithiasis, or kidney stones, is a prevalent condition that prompts numerous patient visits to emergency departments worldwide. The diagnosis of this condition and its complications, such as hydronephrosis, has traditionally relied on various imaging modalities, with ultrasound being a primary, non-invasive option. Recent advancements, including the utilization of the color Doppler twinkling artifact, have potentially enhanced the diagnostic capabilities of ultrasound for nephrolithiasis. Objective: This study aims to evaluate the effectiveness of B-mode sonography and the color Doppler twinkling artifact in the diagnosis of nephrolithiasis and to compare their sensitivity in detecting renal stones and associated hydronephrosis. Methods: A cross-sectional study was conducted at the Department of Radiology, Mahaban Medical and Research Hospital, Topi, Pakistan, from December 2023 to March 2024. Seventy patients presenting with flank pain and aged above 18 years were enrolled. Sonographic examinations were performed using a TOSHIBA Xario Prime ultrasound machine. Data were analyzed using SPSS version 25.0, focusing on the presence of renal stones and hydronephrosis as detected by B-mode sonography and the color Doppler twinkling artifact. Results: Of the 70 patients studied, renal stones were detected in 70% (n=49) via the color Doppler twinkling artifact, while 30% (n=21) showed no stones. Hydronephrosis was observed in 30% (n=21) of patients, with an equal distribution between the left and right kidneys. The mean age of participants was 34.56 years, with a gender distribution of 62.9% male and 37.1% female. The twinkling artifact demonstrated a 100% sensitivity in identifying renal calculi among patients presenting with flank pain. Conclusion: The color Doppler twinkling artifact, when used alongside B-mode sonography, provides a highly sensitive diagnostic tool for nephrolithiasis, underscoring its potential as a primary imaging modality in the evaluation of patients with suspected renal stones. This study highlights the importance of ultrasound in the early detection and management of kidney stones, offering significant implications for improving patient care in healthcare settings lacking advanced imaging technologies.

Compare the Performance of Distinct Neural Networks Techniques to diagnose the kidney stone disease

Artificial Neural Networks are excellent at identifying patterns or trends in data, which makes them perfect for forecasting or prediction. Thus, neural networks have extensive application in biological systems. The application of neural networks to kidney stone diagnosis is emphasized in this article. Kidney stone issues can be diagnosed with neural networks by applying technological concepts such as MLP, SVM, RBF, and BPA. The purpose of this research is to use three different neural network algorithms—each with its own specific design and set of properties to identify kidney stone disease. The performance of the three neural networks is compared in this research with respect to training data set size, model creation time, and accuracy. Kidney stone sickness will be diagnosed using radial basis function (RBF) networks, two layers feed forward perceptrons trained with the back propagation training algorithm, and learning vector quantization (LVQ). However, determining the best approach for any particular diagnostic had never been an easy task. Like many other illnesses, kidney stones have already been diagnosed using neural network algorithms. The main objective of this work is to recommend the best medical diagnostic instrument, such as kidney stone detection, to reduce diagnosis times and improve accuracy and efficiency.

Quantitative susceptibility mapping for detection of kidney stones, hemorrhage differentiation, and cyst classification in ADPKD.

The objective is to demonstrate feasibility of quantitative susceptibility mapping (QSM) in autosomal dominant polycystic kidney disease (ADPKD) patients and to compare imaging findings with traditional T1/T2w magnetic resonance imaging (MRI). Thirty-three consecutive patients (11 male, 22 female) diagnosed with ADPKD were initially selected. QSM images were reconstructed from the multiecho gradient echo data and compared to co-registered T2w, T1w, and CT images. Complex cysts were identified and classified into distinct subclasses based on their imaging features. Prevalence of each subclass was estimated. QSM visualized two renal calcifications measuring 9 and 10mm and three pelvic phleboliths measuring 2mm but missed 24 calcifications measuring 1mm or less and 1 larger calcification at the edge of the field of view. A total of 121 complex T1 hyperintense/T2 hypointense renal cysts were detected. 52 (43%) Cysts appeared hyperintense on QSM consistent with hemorrhage; 60 (49%) cysts were isointense with respect to simple cysts and normal kidney parenchyma, while the remaining 9 (7%) were hypointense. The presentation of the latter two complex cyst subtypes is likely indicative of proteinaceous composition without hemorrhage. Our results indicate that QSM of ADPKD kidneys is possible and uniquely suited to detect large renal calculi without ionizing radiation and able to identify properties of complex cysts unattainable with traditional approaches.

A Novel Transfer Learning Based Kidney Stone Detection Using ResNet50

A Novel Transfer Learning Based Kidney Stone Detection Using ResNet50

Penerapan Artificial Intelligence dalam Mendeteksi Batu Ginjal secara Otomatis pada Citra CT Scan

Background: Kidney stones are a clinical condition with the presence of stones along the urinary tract of varying sizes. The aim of this research is the need for a system to automatically detect kidney stones so that it can help radiologists in diagnosing kidney stones accurately, effectively and efficiently, and patients can immediately undergo further action to cure kidney stones.Methods: The difference in research carried out by researchers is the use of artificial intelligence which uses deep learning with a convolutional neural network (CNN) algorithm. This research uses images obtained from CT scan results from public data (Kaggle) and primary hospital data. The number of images used in the Augmentation training data was 2338 normal images and 2390 kidney stone images. The augmentation testing data used 540 normal images and 446 kidney stone images. The research also involved experts, namely radiology specialists, in determining images with abnormal and normal stone tones.Results: research obtained from CT Scan images of kidney stones with augmentation and original using public data/Kaggle images, obtained using augmentation obtained a high accuracy value of 99.69%. Meanwhile, in testing data using primary/hospital data images, augmented data obtained accuracy values that were still low at 45.43% and 45.23%, respectively.Conclusions: The use of deep learning with the CNN model in training data augmentation obtained high accuracy values, however in testing data using hospital CT scan images the accuracy value was still low, but it was able to recognize images of kidney stones, so it could help in automatically diagnosing kidney stones. For future work could involve refining the model to handle variations in hospital data or exploring additional features to improve generalizability.

CHRONIC KIDNEY DISEASE DETECTION

This paper presents an innovative approach to detecting kidney stones using ultrasound images. We address a significant challenge in healthcare by applying advanced image processing and machine learning techniques to enhance the detection and classification of kidney stones. Our study begins with a thorough literature review that evaluates the current state of technology and identifies the limitations of existing kidney stone detection systems. Based on this review, we propose a novel system that addresses these limitations, offering improved accuracy and timeliness in diagnosis. This new approach is designed to advance patient care by providing more reliable and prompt detection of kidney stones, ultimately contributing to better clinical outcomes.