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

Using ensemble learning and hierarchical strategy to predict the outcomes of ESWL for upper ureteral stone treatment

Urinary tract stones are a common and frequently recurring medical issue. Accurately predicting the success rate after surgery can help avoid ineffective medical procedures and reduce unnecessary healthcare costs. This study collected data from patients with upper ureter stones who underwent extracorporeal shock wave lithotripsy, including cases of successful as well as unsuccessful stone removal after the first and second lithotripsy procedures, and constructed prediction systems for the outcomes of the first and second lithotripsy procedures. Features were extracted from three categories of information: patient characteristics, stone characteristics, and extracorporeal shock wave lithotripsy machine data, and additional features were created using Feature Creation. Finally, the impact of features on the models was analyzed using six methods to calculate feature importance. Our prediction model for the first lithotripsy, selected from among 43 methods and seven ensemble learning techniques, achieves an AUC of 0.91. For the second lithotripsy, the AUC reaches 0.76. The results indicate that the detailed and binary information provided by patients regarding their history of stone experiences contributes differently to the predictive accuracy of the first and second lithotripsy procedures. The prediction tool is available at https://predictor.isu.edu.tw/ks.

Trends of "Artificial Intelligence, Machine Learning, Virtual Reality, and Radiomics in Urolithiasis" over the Last 30 Years (1994-2023) as Published in the Literature (PubMed): A Comprehensive Review

Purpose: To analyze the bibliometric publication trend on the application of "Artificial Intelligence (AI) and its subsets (Machine Learning-ML, Virtual reality-VR, Radiomics) in Urolithiasis" over 3 decades. We looked at the publication trends associated with AI and stone disease, including both clinical and surgical applications, and training in endourology. Methods: Through a MeshTerms research on PubMed, we performed a comprehensive review from 1994-2023 for all published articles on "AI, ML, VR, and Radiomics." Articles were then divided into three categories as follows: A-Clinical (Nonsurgical), B-Clinical (Surgical), and C-Training articles, and articles were then assigned to following three periods: Period-1 (1994-2003), Period-2 (2004-2013), and Period-3 (2014-2023). Results: A total of 343 articles were noted (Groups A-129, B-163, and C-51), and trends increased from Period-1 to Period-2 at 123% (p = 0.009) and to period-3 at 453% (p = 0.003). This increase from Period-2 to Period-3 for groups A, B, and C was 476% (p = 0.019), 616% (0.001), and 185% (p < 0.001), respectively. Group A articles included rise in articles on "stone characteristics" (+2100%; p = 0.011), "renal function" (p = 0.002), "stone diagnosis" (+192%), "prediction of stone passage" (+400%), and "quality of life" (+1000%). Group B articles included rise in articles on "URS" (+2650%, p = 0.008), "PCNL"(+600%, p = 0.001), and "SWL" (+650%, p = 0.018). Articles on "Targeting" (+453%, p < 0.001), "Outcomes" (+850%, p = 0.013), and "Technological Innovation" (p = 0.0311) had rising trends. Group C articles included rise in articles on "PCNL" (+300%, p = 0.039) and "URS" (+188%, p = 0.003). Conclusion: Publications on AI and its subset areas for urolithiasis have seen an exponential increase over the last decade, with an increase in surgical and nonsurgical clinical areas, as well as in training. Future AI related growth in the field of endourology and urolithiasis is likely to improve training, patient centered decision-making, and clinical outcomes.

Artificial intelligence and clinical guidance in male reproductive health: ChatGPT4.0's AUA/ASRM guideline compliance evaluation

BACKGROUND

Male infertility is defined as the inability of a male to achieve a pregnancy in a fertile female by the American Urological Association (AUA) and the American Society for Reproductive Medicine (ASRM). Artificial intelligence, particularly in language processing models like ChatGPT4.0, offers new possibilities for supporting clinical decision-making. This study aims to assess the effectiveness of ChatGPT4.0 in responding to clinical queries regarding male infertility, which is aligned with AUA/ASRM guidelines.

METHODS

This observational study employed a design to evaluate the performance of ChatGPT4.0 across 1073 structured clinical queries categorized into true/false, multiple-choice, and open-ended. Two independent reviewers specializing in reproductive medicine assessed the responses using a six-point Likert scale to evaluate accuracy, relevance, and guideline adherence.

RESULTS

In the true/false category, the initial accuracy was 92%, which increased to 94% by the end of the study period. For multiple-choice questions, accuracy improved from 85% to 89%. The most significant gains were seen in open-ended questions, where accuracy rose from 78% to 86%. Initially, some responses did not fully align with the AUA/ASRM guidelines. However, by the end of the 60 days, these responses had become more comprehensive and clinically relevant, indicating an improvement in the model's ability to generate guideline-conformant answers (p < 0.05). The depth and accuracy of responses for higher difficulty questions also showed enhancement (p < 0.01).

CONCLUSION

ChatGPT4.0 can serve as a valuable support tool in managing male infertility, providing reliable, guideline-based information that enhances the accuracy of clinical decision-making tools and supports patient education.

Development of a new scoring system predicting medical expulsive therapy success on 4-10 mm distal ureteral stones: medical expulsive therapy stone score (METSS)

Ureteral stone passage by using medical expulsive therapy (MET) are affected by numerous radiological and clinical parameters. We aimed to construct a scoring system, which would be based on clinical and computed tomography (CT)-derived data, to predict the success of the MET approach. 186 patients presenting to urology clinic or emergency department with unilateral single 4-10 mm distal ureteral stone and who had MET were included. All patients were divided into two groups as the MET-successful group and the MET-unsuccessful group. The success rate of MET was 67.2%. Stone size ≥ 6.5 mm, stone density > 1078 HU, ureteral wall thickness (UWT) > 2.31 mm, ureteral diameter (UD) > 9.24 mm, presence of periureteral stranding (PUS) and presence of diabetes mellitus (DM) were stated as the independent risk factors. Based on the regression coefficients on multivariate logistic regression analysis, 1 point for stone size > 6.5 mm, 2 points for stone density > 1078 HU, 2 points for UWT > 2.31 mm, 3 points for UD > 9.24 mm, 1 point for presence of PUS and 1 point for presence of DM were assigned to patients for each risk factor. Higher medical expulsive therapy stone score (METSS) indicated lower MET success. All patients were classified into three risk groups according to METSS: low risk (0-3 points; success percentage: 92.8%); intermediate risk (4-5 points; success percentage: 60.4%) and high risk (6-10 points; success percentage: 8.3%). The METSS seems to separate successfully the patients with a favorable or adverse constellation of factors.

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.