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

Improving Patient Information and Enhanced Consent in Urology: The Impact of Simulation and Multimedia Tools. A Systematic Literature Review from the European Association of Urology Patient Office

BACKGROUND AND OBJECTIVE

Discussions surrounding urological diagnoses and planned procedures can be challenging, and patients might experience difficulty in understanding the medical language, even when shown radiological imaging or drawings. With the introduction of virtual reality and simulation, informed consent could be enhanced by audiovisual content and interactive platforms. Our aim was to assess the role of enhanced consent in the field of urology.

METHODS

A systematic review of the literature was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, using informed consent, simulation, and virtual reality in urology as the search terms. All original articles were screened.

KEY FINDINGS AND LIMITATIONS

Thirteen original studies were included in the review. The overall quality of these studies was deemed good according to the Newcastle-Ottawa Scale. The studies analysed the application of different modalities for enhanced consent: 3D printed or digital models, audio visual multimedia contents, virtual simulation of procedures and interactive navigable apps. Published studies agreed upon a significantly improved effect on patient understanding of the diagnosis, including basic anatomical details, and surgery-related issues such as the aim, steps and the risks connected to the planned intervention. Patient satisfaction was unanimously reported as improved as a result of enhanced consent.

CONCLUSIONS AND CLINICAL IMPLICATIONS

Simulation and multimedia tools are extremely valuable for improving patients' understanding of and satisfaction with urological procedures. Widespread application of enhanced consent would represent a milestone for patient-urologist communication.

Consensus statement addressing controversies and guidelines on pediatric urolithiasis

PURPOSE

We aimed to investigate controversial pediatric urolithiasis issues systematically, integrating expert consensus and comprehensive guidelines reviews.

METHODS

Two semi-structured online focus group meetings were conducted to discuss the study's need and content, review current literature, and prepare the initial survey. Data were collected through surveys and focus group discussions. Existing guidelines were reviewed, and a second survey was conducted using the Delphi method to validate findings and facilitate consensus. The primary outcome measures investigated controversial issues, integrating expert consensus and guideline reviews.

RESULTS

Experts from 15 countries participated, including 20 with 16+ years of experience, 2 with 11-15 years, and 4 with 6-10 years. The initial survey identified nine main themes, emphasizing the need for standardized diagnostic and treatment protocols and tailored treatments. Inter-rater reliability was high, with controversies in treatment approaches (score 4.6, 92% agreement), follow-up protocols (score 4.8, 100% agreement), and diagnostic criteria (score 4.6, 92% agreement). The second survey underscored the critical need for consensus on identification, diagnostic criteria (score 4.6, 92% agreement), and standardized follow-up protocols (score 4.8, 100% agreement).

CONCLUSION

The importance of personalized treatment in pediatric urolithiasis is clear. Prioritizing low-radiation diagnostic tools, effectively managing residual stone fragments, and standardized follow-up protocols are crucial for improving patient outcomes. Integrating new technologies while ensuring safety and reliability is also essential. Harmonizing guidelines across regions can provide consistent and effective management. Future efforts should focus on collaborative research, specialized training, and the integration of new technologies in treatment protocols.

Can Machine Learning Correctly Predict Outcomes of Flexible Ureteroscopy with Laser Lithotripsy for Kidney Stone Disease? Results from a Large Endourology University Centre

Background and objective

The integration of machine learning (ML) in health care has garnered significant attention because of its unprecedented opportunities to enhance patient care and outcomes. In this study, we trained ML algorithms for automated prediction of outcomes of ureteroscopic laser lithotripsy (URSL) on the basis of preoperative characteristics.

Methods

Data were retrieved for patients treated with ureteroscopy for urolithiasis by a single experienced surgeon over a 7-yr period. Sixteen ML classification algorithms were trained to investigate correlation between preoperative characteristics and postoperative outcomes. The outcomes assessed were primary stone-free status (SFS, defined as the presence of only stone fragments <2 mm on endoscopic visualisation and at 3-mo imaging) and postoperative complications. An ensemble model was constructed from the best-performing algorithms for prediction of complications and for prediction of SFS. Simultaneous prediction of postoperative characteristics was then investigated using a multitask neural network, and explainable artificial intelligence (AI) was used to demonstrate the predictive power of the best models.

Key findings and limitations

An ensemble ML model achieved accuracy of 93% and precision of 87% for prediction of SFS. Complications were mainly associated with a preoperative positive urine culture (1.44). Logistic regression revealed that SFS was impacted by the total stone burden (0.34), the presence of a preoperative stent (0.106), a positive preoperative urine culture (0.14), and stone location (0.09). Explainable AI results emphasised the key features and their contributions to the output.

Conclusions and clinical implications

Technological advances are helping urologists to overcome the classic limits of ureteroscopy, namely stone size and the risk of complications. ML represents an excellent aid for correct prediction of outcomes after training on pre-existing data sets. Our ML model achieved accuracy of >90% for prediction of SFS and complications, and represents a basis for the development of an accessible predictive model for endourologists and patients in the URSL setting.

Patient summary

We tested the ability of artificial intelligence to predict treatment outcomes for patients with kidney stones. We trained 16 different machine learning tools with data before surgery, such as patient age and the stone characteristics. Our final model was >90% accurate in predicting stone-free status after surgery and the occurrence of complications.

Evaluation of ChatGPT for Patient Counseling in Kidney Stone Clinic: A Prospective Study

Introduction: The potential of large language models (LLMs) is to improve the clinical workflow and to make patient care efficient. We prospectively evaluated the performance of the LLM ChatGPT as a patient counseling tool in the urology stone clinic and validated the generated responses with those of urologists. Methods: We collected 61 questions from 12 kidney stone patients and prompted those to ChatGPT and a panel of experienced urologists (Level 1). Subsequently, the blinded responses of urologists and ChatGPT were presented to two expert urologists (Level 2) for comparative evaluation on preset domains: accuracy, relevance, empathy, completeness, and practicality. All responses were rated on a Likert scale of 1 to 10 for psychometric response evaluation. The mean difference in the scores given by the urologists (Level 2) was analyzed and interrater reliability (IRR) for the level of agreement in the responses between the urologists (Level 2) was analyzed by Cohen's kappa. Results: The mean differences in average scores between the responses from ChatGPT and urologists showed significant differences in accuracy (p < 0.001), empathy (p < 0.001), completeness (p < 0.001), and practicality (p < 0.001), except for the relevance domain (p = 0.051), with ChatGPT's responses being rated higher. The IRR analysis revealed significant agreement only in the empathy domain [k = 0.163, (0.059-0.266)]. Conclusion: We believe the introduction of ChatGPT in the clinical workflow could further optimize the information provided to patients in a busy stone clinic. In this preliminary study, ChatGPT supplemented the answers provided by the urologists, adding value to the conversation. However, in its current state, it is still not ready to be a direct source of authentic information for patients. We recommend its use as a source to build a comprehensive Frequently Asked Questions bank as a prelude to developing an LLM Chatbot for patient counseling.