Detection of Renal Calculi in Ultrasound Image Using Meta-Heuristic Support Vector Machine

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

Objective

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

Methods

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

Results

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

Conclusion

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

Clinical Applications of Machine Learning for Urolithiasis and Benign Prostatic Hyperplasia: A Systematic Review

INTRODUCTION

Previous systematic reviews related to machine learning (ML) in urology often overlooked the literature related to endourology. Therefore, we aim to conduct a more focused systematic review examining the use of ML algorithms for benign prostatic hyperplasia (BPH) or urolithiasis. In addition, we are the first group to evaluate these articles using the STREAM-URO framework.

METHODS

Searches of MEDLINE, Embase, and the Cochrane CENTRAL databases were conducted from inception through July 12, 2021. Keywords included those related to ML, endourology, urolithiasis, and BPH. Two reviewers screened the citations that were eligible for title, abstract and full-text screening, with conflicts resolved by a third reviewer. Two reviewers extracted information from the studies, with discrepancies resolved by a third reviewer. The data collected was then qualitatively synthesized by consensus. Two reviewers evaluated each article according to the STREAM-URO checklist with discrepancies resolved by a third reviewer.

RESULTS

After identifying 459 unique citations, 63 articles were retained for data extraction. Most articles consisted of tabular (n=32) and computer vision (n=23) tasks. The two most common problem types were classification (n=40) and regression (n=12). In general, most studies utilized neural networks as their ML algorithm (n=36). Among the 63 studies retrieved, 58 were related to urolithiasis and five focused on BPH. The urolithiasis studies were designed for outcome prediction (n=20), stone classification (n=18), diagnostics (n=17), and therapeutics (n=3). The BPH studies were designed for outcome prediction (n=2), diagnostics (n=2), and therapeutics (n=1). On average, the urolithiasis and BPH articles met 13.8 (SD 2.6), and 13.4 (4.1) of the 26 STREAM-URO framework criteria, respectively.

CONCLUSIONS

The majority of the retrieved studies successfully helped with outcome prediction, diagnostics, and therapeutics for both urolithiasis and BPH. While ML shows great promise in improving patient care, it is important to adhere to the recently developed STREAM-URO framework to ensure the development of high-quality ML studies.

Artificial intelligence in endourology: emerging technology for individualized care

PURPOSE OF REVIEW

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

RECENT FINDINGS

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

SUMMARY

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

The Ascent of Artificial Intelligence in Endourology: a Systematic Review Over the Last 2 Decades

Purpose of Review

To highlight and review the application of artificial intelligence (AI) in kidney stone disease (KSD) for diagnostics, predicting procedural outcomes, stone passage, and recurrence rates. The systematic review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) checklist.

Recent Findings

This review discusses the newer advancements in AI-driven management strategies, which holds great promise to provide an essential step for personalized patient care and improved decision making. AI has been used in all areas of KSD including diagnosis, for predicting treatment suitability and success, basic science, quality of life (QOL), and recurrence of stone disease. However, it is still a research-based tool and is not used universally in clinical practice. This could be due to a lack of data infrastructure needed to train the algorithms, wider applicability in all groups of patients, complexity of its use and cost involved with it.

Summary

The constantly evolving literature and future research should focus more on QOL and the cost of KSD treatment and develop evidence-based AI algorithms that can be used universally, to guide urologists in the management of stone disease.

Computer-aided diagnosis system for the classification of multi-class kidney abnormalities in the noisy ultrasound images

BACKGROUND AND OBJECTIVE

The primary causes of kidney failure are chronic and polycystic kidney diseases. Cyst, stone, and tumor development lead to chronic kidney diseases that commonly impair kidney functions. The kidney diseases are asymptomatic and do not show any significant symptoms at its initial stage. Therefore, diagnosing the kidney diseases at their earlier stage is required to prevent the loss of kidney function and kidney failure.

METHODS

This paper proposes a computer-aided diagnosis (CAD) system for detecting multi-class kidney abnormalities from ultrasound images. The presented CAD system uses a pre-trained ResNet-101 model for extracting the features and support vector machine (SVM) classifier for the classification purpose. Ultrasound images usually gets affected by speckle noise that degrades the image quality and performance of the CAD system. Hence, it is necessary to remove speckle noise from the ultrasound images. Therefore, a CAD based system is proposed with the despeckling module using a deep residual learning network (RLN) to reduce speckle noise. Pre-processing of ultrasound images using deep RLN helps to drastically improve the classification performance of the CAD system. The proposed CAD system achieved better prediction results when compared to the existing state-of-the-art methods.

RESULTS

To validate the proposed CAD system performance, the experiments have been carried out in the noisy kidney ultrasound images. The designed system framework achieved the maximum classification accuracy when compared to the existing approaches. The SVM classifier is selected for the CAD system based on performance comparison with various classifiers like K-nearest neighbour, tree, discriminant, Naive Bayes, and linear.

CONCLUSIONS

The proposed CAD system outperforms in classifying the noisy kidney ultrasound images precisely as compared to the existing state-of-the-art methods. Further, the CAD system is evaluated in terms of selectivity and sensitivity scores. The presented CAD system with the pre-processing module would serve as a real-time supporting tool for diagnosing multi-class kidney abnormalities from the ultrasound images.

An ensemble of deep neural networks for kidney ultrasound image classification

BACKGROUND AND OBJECTIVE

Chronic kidney disease is a worldwide health issue which includes not only kidney failure but also complications of reduced kidney functionality. Cyst formation, nephrolithiasis or kidney stone, and renal cell carcinoma or kidney tumor are the common kidney disorders which affects the functionality of kidneys. These disorders are typically asymptomatic, therefore early and automatic diagnosis of kidney disorders are required to avoid serious complications.

METHODS

This paper proposes an automatic classification of B-mode kidney ultrasound images based on the ensemble of deep neural networks (DNNs) using transfer learning. The ultrasound images are usually affected by speckle noise and quality selection in the ultrasound image is based on perception-based image quality evaluator score. Three variant datasets are given to the pre-trained DNN models for feature extraction followed by support vector machine for classification. The ensembling of different pre-trained DNNs like ResNet-101, ShuffleNet, and MobileNet-v2 are combined and final predictions are done by using the majority voting technique. By combining the predictions from multiple DNNs the ensemble model shows better classification performance than the individual models. The presented method proved its superiority when compared to the conventional and DNN based classification methods. The developed ensemble model classifies the kidney ultrasound images into four classes, namely, normal, cyst, stone, and tumor.

RESULTS

To highlight effectiveness of the proposed approach, the ensemble based approach is compared with the existing state-of-the-art methods and tested in the variants of ultrasound images like in quality and noisy conditions. The presented method resulted in maximum classification accuracy of 96.54% in testing with quality images and 95.58% in testing with noisy images. The performance of the presented approach is evaluated based on accuracy, sensitivity, and selectivity.

CONCLUSIONS

From the experimental analysis, it is clear that the ensemble of DNNs classifies the majority of images correctly and results in maximum classification accuracy as compared to the existing methods. This automatic classification approach is a supporting tool for the radiologists and nephrologists for precise diagnosis of kidney diseases.