A Study on the Optimal Artificial Intelligence Model for Determination of Urolithiasis

An Artificial Intelligence-assisted Diagnostic System Improves Upper Urine Tract Cytology Diagnosis

BACKGROUND/AIM

To evaluate efficacy of the AIxURO system, a deep learning-based artificial intelligence (AI) tool, in enhancing the accuracy and reliability of urine cytology for diagnosing upper urinary tract cancers.

MATERIALS AND METHODS

One hundred and eighty-five cytology samples of upper urine tract were collected and categorized according to The Paris System for Reporting Urinary Cytology (TPS), yielding 168 negative for High-Grade Urothelial Carcinoma (NHGUC), 14 atypical urothelial cells (AUC), 2 suspicious for high-grade urothelial carcinoma (SHGUC), and 1 high-grade urothelial carcinoma (HGUC). The AIxURO system, trained on annotated cytology images, was employed to analyze these samples. Independent assessments by a cytotechnologist and a cytopathologist were conducted to validate the initial AIxURO assessment.

RESULTS

AIxURO identified discrepancies in 37 of the 185 cases, resulting in a 20% discrepancy rate. The cytotechnologist achieved an accuracy of 85% for NHGUC and 21.4% for AUC, whereas the cytopathologist attained accuracies of 95% for NHGUC and 85.7% for AUC. The cytotechnologist exhibited overcall rates of roughly 15% and undercall rates of greater than 50%, while the cytopathologist showed profoundly lower miscall rates from both undercall and overcall. AIxURO significantly enhanced diagnostic accuracy and consistency, particularly in complex cases involving atypical cells.

CONCLUSION

AIxURO can improve the accuracy and reliability of cytology diagnosis for upper urine tract urothelial carcinomas by providing precise detection on atypical urothelial cells and reducing subjectivity in assessments. The integration of AIxURO into clinical practice can significantly ameliorate diagnostic outcomes, highlighting the synergistic potential of AI technology and human expertise in cytology.

Artificial intelligence in urolithiasis: a systematic review of utilization and effectiveness

PURPOSE

Mirroring global trends, artificial intelligence advances in medicine, notably urolithiasis. It promises accurate diagnoses, effective treatments, and forecasting epidemiological risks and stone passage. This systematic review aims to identify the types of AI models utilised in urolithiasis studies and evaluate their effectiveness.

METHODS

The study was registered with PROSPERO. Pubmed, EMBASE, Google Scholar, and Cochrane Library databases were searched for relevant literature, using keywords such as 'urology,' 'artificial intelligence,' and 'machine learning.' Only original AI studies on urolithiasis were included, excluding reviews, unrelated studies, and non-English articles. PRISMA guidelines followed.

RESULTS

Out of 4851 studies initially identified, 71 were included for comprehensive analysis in the application of AI in urolithiasis. AI showed notable proficiency in stone composition analysis in 12 studies, achieving an average precision of 88.2% (Range 0.65-1). In the domain of stone detection, the average precision remarkably reached 96.9%. AI's accuracy rate in predicting spontaneous ureteral stone passage averaged 87%, while its performance in treatment modalities such as PCNL and SWL achieved average accuracy rates of 82% and 83%, respectively. These AI models were generally superior to traditional diagnostic and treatment methods.

CONCLUSION

The consolidated data underscores AI's increasing significance in urolithiasis management. Across various dimensions-diagnosis, monitoring, and treatment-AI outperformed conventional methodologies. High precision and accuracy rates indicate that AI is not only effective but also poised for integration into routine clinical practice. Further research is warranted to establish AI's long-term utility and to validate its role as a standard tool in urological care.

Automatic kidney stone identification: an adaptive feature-weighted LSTM model based on urine and blood routine analysis

Kidney stones are the most common urinary system diseases, and early identification is of great significance. The purpose of this study was to use routine urine and blood detection indices to build a deep learning (DL) model to identify the presence of kidney stones in the early stage. A retrospective analysis was conducted on patients with kidney stones who were treated at West China Hospital of Sichuan University from January 2020 to June 2023. A total of 1130 individuals presenting with kidney stones and 1230 healthy subjects were enrolled. The first blood and urine laboratory data of participants at our hospital were collected, and the data were divided into a training dataset (80%) and a verification dataset (20%). Additionally, a long short-term memory (LSTM)-based adaptive feature weighting model was trained for the early identification of kidney stones, and the results were compared with those of other models. The performance of the model was evaluated by the area under the subject working characteristic curve (AUC). The important predictive factors are determined by ranking the characteristic importance of the predictive factors. A total of 17 variables were screened; among the top 4 characteristics according to the weight coefficient in this model, urine WBC, urine occult blood, qualitative urinary protein, and microcyte percentage had high predictive value for kidney stones in patients. The accuracy of the kidney stone (KS-LSTM) learning model was 89.5%, and the AUC was 0.95. Compared with other models, it has better performance. The results show that the KS-LSTM model based on routine urine and blood tests can accurately identify the presence of kidney stones. And provide valuable assistance for clinicians to identify kidney stones in the early stage.

Serious Games as a Therapeutic Tool in Pediatric Urology: A Review of Current Applications and Future Directions

This paper examines the use of serious games that integrate engaging gameplay with educational and therapeutic benefits with a particular focus on their application in urology. The study reviews both domestic and international cases to evaluate the current practices and implications of these digital therapies. While serious games have been widely utilized in the treatment of pediatric cancer and psychiatric disorders-areas that require long-term care and management-their application in urology, in particular pediatric urology, has been limited. Although digital therapies like serious games are still emerging in the field of urology, they hold promise as effective supplements to traditional treatments while offering new options for managing a range of urological conditions. Pediatric conditions such as bedwetting, urinary incontinence, and dysuria, which often benefit from long-term training and habit formation, are particularly well-suited to this approach. By helping pediatric patients better understand and manage their symptoms, serious games can play a supportive role in the treatment process and have shown positive medical outcomes. This paper reviews the definitions and therapeutic effects of serious games, analyzes their current use in the medical field, and argues for their increased application in treating pediatric urinary conditions.

Acoustic emission of kidney stones: a medical adaptation of statistical breakdown mechanisms

Kidney stones have a prevalence rate of > 10% in some countries. There has been a significant increase in surgery to treat kidney stones over the last 10 years, and it is crucial that such techniques are as effective as possible, while limiting complications. A selection of kidney stones with different chemical and structural properties were subjected to compression. Under compression, they emit acoustic signals called crackling noise. The variability of the crackling noise was surprisingly great comparing weddellite, cystine and uric acid stones. Two types of signals were found in all stones. At high energies of the emitted sound waves, we found avalanche behaviour, while all stones also showed signals of local, uncorrelated collapse. These two types of events are called 'wild' for avalanches and 'mild' for uncorrelated events. The key observation is that the crossover from mild to wild collapse events differs greatly between different stones. Weddellite showed brittle collapse, extremely low crossover energies (< 5 aJ) and wild avalanches over 6 orders of magnitude. In cystine and uric acid stones, the collapse was more complicated with a dominance of local "mild" breakings, although they all contained some stress-induced collective avalanches. Cystine stones had high crossover energies, typically [Formula: see text] 750 aJ, and a narrow window over which they showed wild avalanches. Uric acid stones gave moderate values of crossover energies, [Formula: see text] 200 aJ, and wild avalanche behaviour for [Formula: see text] 3 orders of magnitude. Further research extended to all stone types, and measurement of stone responses to different lithotripsy strategies, will assist in optimisation of settings of the laser and other lithotripsy devices to insight fragmentation by targeting the 'wild' avalanche regime.

The Current State of Artificial Intelligence Application in Urology

Artificial intelligence (AI) is being used in many areas of healthcare, including disease diagnosis and personalized treatment and rehabilitation management. Medical AI research and development has primarily focused on diagnosis, prediction, treatment, and management as an aid to patient care. AI is being utilized primarily in the areas of personal healthcare and diagnostic imaging. In the field of urology, significant investments are being made in the development of urination monitoring systems in the field of personal healthcare and ureteral stricture and urinary stone diagnosis solutions in the field of diagnostic imaging. In addition, AI technology is also being applied in the field of neurogenic bladder to develop risk monitoring systems based on video and audio data. This paper examines the application of AI to urological diseases and discusses the current trends and future prospects of AI research.

General Overview of Artificial Intelligence for Interstitial Cystitis in Urology

Our understanding of interstitial cystitis/bladder pain syndrome (IC/BPS) has evolved over time. The diagnosis of IC/BPS is primarily based on symptoms such as urgency, frequency, and bladder or pelvic pain. While the exact causes of IC/BPS remain unclear, it is thought to involve several factors, including abnormalities in the bladder's urothelium, mast cell degranulation within the bladder, inflammation of the bladder, and altered innervation of the bladder. Treatment options include patient education, dietary and lifestyle modifications, medications, intravesical therapy, and surgical interventions. This review article provides insights into IC/BPS, including aspects of treatment, prognosis prediction, and emerging therapeutic options. Additionally, it explores the application of deep learning for diagnosing major diseases associated with IC/BPS.

Improved Detection of Urolithiasis Using High-Resolution Computed Tomography Images by a Vision Transformer Model

PURPOSE

Urinary stones cause lateral abdominal pain and are a prevalent condition among younger age groups. The diagnosis typically involves assessing symptoms, conducting physical examinations, performing urine tests, and utilizing radiological imaging. Artificial intelligence models have demonstrated remarkable capabilities in detecting stones. However, due to insufficient datasets, the performance of these models has not reached a level suitable for practical application. Consequently, this study introduces a vision transformer (ViT)-based pipeline for detecting urinary stones, using computed tomography images with augmentation.

METHODS

The super-resolution convolutional neural network (SRCNN) model was employed to enhance the resolution of a given dataset, followed by data augmentation using CycleGAN. Subsequently, the ViT model facilitated the detection and classification of urinary tract stones. The model's performance was evaluated using accuracy, precision, and recall as metrics.

RESULTS

The deep learning model based on ViT showed superior performance compared to other existing models. Furthermore, the performance increased with the size of the backbone model.

CONCLUSION

The study proposes a way to utilize medical data to improve the diagnosis of urinary tract stones. SRCNN was used for data preprocessing to enhance resolution, while CycleGAN was utilized for data augmentation. The ViT model was utilized for stone detection, and its performance was validated through metrics such as accuracy, sensitivity, specificity, and the F1 score. It is anticipated that this research will aid in the early diagnosis and treatment of urinary tract stones, thereby improving the efficiency of medical personnel.

Development and Evaluation of Urolithiasis Detection Technology Based on a Multimethod Algorithm

Purpose: In this paper, we propose an optimal ureter stone detection model utilizing multiple artificial intelligence technologies. Specifically, the proposed model of urinary tract stone detection merges an artificial intelligence model and an image processing model, resulting in a multimethod approach.Methods: We propose an optimal urinary tract stone detection algorithm based on artificial intelligence technology. This method was intended to increase the accuracy of urinary tract stone detection by combining deep learning technology (Fast R-CNN) and image processing technology (Watershed).Results: As a result of deriving the confusion matrix, the sensitivity and specificity of urinary tract stone detection were calculated to be 0.90 and 0.91, and the accuracy for their position was 0.84. This value was higher than 0.8, which is the standard for accuracy. This finding confirmed that accurate guidance to the stones area was possible when the developed platform was used to support actual surgery.Conclusions: The performance evaluation of the method proposed herein indicated that it can effectively play an auxiliary role in diagnostic decision-making with a clinically acceptable range of safety. In particular, in the case of ambush stones or urinary stones accompanying ureter polyps, the value that could be obtained through combination therapy based on diagnostic assistance could be evaluated.

New Trends in Innovative Technologies Applying Artificial Intelligence to Urinary Diseases

Artificial intelligence (AI) is used in various fields of medicine, with applications encompassing all areas of medical services, such as the development of medical robots, the diagnosis and personalized treatment of diseases, and personalized healthcare. Medical AI research and development have been largely focused on diagnosis, prediction, treatment, and management as an auxiliary means of patient care. AI is mainly used in the fields of personal healthcare and diagnostic imaging. In urology, substantial investments are being made in the development of urination monitoring systems in the personal healthcare field and diagnostic solutions for ureteral stricture and urolithiasis in the diagnostic imaging field. This paper describes AI applications for urinary diseases and discusses current trends and future perspectives in AI research.