Analysis of Polymer-Ceramic Composites Performance on Electrical and Mechanical Properties through Finite Element and Empirical Models.

Polymer and ceramic-based composites offer a unique blend of desirable traits for improving dielectric permittivity. This study employs an empirical approach to estimate the dielectric permittivity of composite materials and uses a finite element model to understand the effects of permittivity and filler concentration on mechanical and electrical properties. The empirical model combines the Maxwell-Wagner-Sillars (MWS) and Bruggeman models to estimate the effective permittivity using Barium Titanate (BT) and Calcium Copper Titanate Oxide (CCTO) as ceramic fillers dispersed in a Polydimethylsiloxane (PDMS) polymer matrix. Results indicate that the permittivity of the composite improves with increased filler content, with CCTO/PDMS emerging as the superior combination for capacitive applications. Capacitance and energy storage in the CCTO/PDMS composite material reached 900 nF and 450 nJ, respectively, with increased filler content. Additionally, increased pressure on the capacitive model with varied filler content showed promising effects on mechanical properties. The interaction between BT filler and the polymer matrix significantly altered the electrical properties of the model, primarily depending on the composite's permittivity. This study provides comprehensive insights into the effects of varied filler concentrations on estimating mechanical and electrical properties, aiding in the development of real-world pressure-based capacitive models.

Telemedicine in Endourology for Patient Management and Healthcare Delivery: Current Status and Future Perspectives.

PURPOSE OF REVIEW: Researchers have examined how telemedicine affects endourological patients. This review analyzes the literature to determine telemedicine's benefits and limitations in endourology. RECENT FINDINGS: Many studies were devoted to describing the effect of telemedicine on endourological patient satisfaction, optimization of the clinical decision-making among patients with kidney and ureteric stones, the effectiveness of telemedicine in the management of patients with indications for PCNL, follow-up for patients with urolithiasis and describing financial effectiveness for the patients after BOO surgery. The authors describe phone calls, video calls, and online booking platforms as used as telemedicine technology. However, several concerns also exist, such as the necessity of internet connections and appropriate devices, different receptivity among certain subgroups, data safety, and different regulatory environments among countries. Telemedicine offers the potential to reduce patient travel time, expedite decision-making, and save costs in endourology. However, its everyday implementation is challenging due to various obstacles faced by patients and providers, hindering the realization of its full potential and necessitating a systematic approach to problem-solving.

A Machine Learning Predictive Model for Ureteroscopy Lasertripsy Outcomes in a Pediatric Population-Results from a Large Endourology Tertiary Center.

Introduction: We aimed to develop machine learning (ML) algorithms for the automated prediction of postoperative ureteroscopy outcomes for pediatric kidney stones based on preoperative characteristics. Materials and Methods: Data from pediatric patients who underwent ureteroscopy for stone treatment by a single experienced surgeon, between 2010 and 2023 in Southampton General Hospital, were retrospectively collected. Fifteen ML classification algorithms were used to investigate correlations between preoperative characteristics and postoperative outcomes: primary stone-free status (SFS, defined as stone fragments <2 mm at the end of the procedure confirmed endoscopically and no evidence of stone fragments >2 mm at Xray kidney-ureters-bladder (XR KUB) or ultrasound kidney-ureters-bladder (US KUB) at 3 months follow-up) and complications. For the task of complication and stone status, an ensemble model was made out of Bagging classifier, Extra Trees classifier, and linear discriminant analysis. Also, a multitask neural network was constructed for the simultaneous prediction of all postoperative characteristics. Finally, explainable artificial intelligence techniques were used to explain the prediction made by the best models. Results: The ensemble model produced the highest accuracy (90%) in predicting SFS, finding correlation with overall stone size (-0.205), presence of multiple stones (-0.127), and preoperative stenting (-0.102). Complications were predicted by Synthetic Minority Oversampling Technique (SMOTE) oversampled dataset (93.3% accuracy) with relation to preoperative positive urine culture (-0.060) and SFS (0.003). Training ML for the multitask model, accuracies of 83.3% and 80% were respectively reached. Conclusion: ML has a great potential of assisting health care research, with possibilities to investigate dataset at a higher level. With the aid of this intelligent tool, urologists can implement their practice and develop new strategies for outcome prediction and patient counseling and informed shared decision-making. Our model reached an excellent accuracy in predicting SFS and complications in the pediatric population, leading the way to the validation of patient-specific predictive tools.

Optimizing Capacitive Pressure Sensor Geometry: A Design of Experiments Approach with a Computer-Generated Model.

This study presents a comprehensive investigation into the design and optimization of capacitive pressure sensors (CPSs) for their integration into capacitive touch buttons in electronic applications. Using the Finite Element Method (FEM), various geometries of dielectric layers were meticulously modeled and analyzed for their capacitive and sensitivity parameters. The flexible elastomer polydimethylsiloxane (PDMS) is used as a diaphragm, and polyvinylidene fluoride (PVDF) is a flexible material that acts as a dielectric medium. The Design of Experiment (DoE) techniques, aided by statistical analysis, were employed to identify the optimal geometric shapes of the CPS model. From the prediction using the DoE approach, it is observed that the cylindrical-shaped dielectric medium has better sensitivity. Using this optimal configuration, the CPS was further examined across a range of dielectric layer thicknesses to determine the capacitance, stored electrical energy, displacement, and stress levels at uniform pressures ranging from 0 to 200 kPa. Employing a 0.1 mm dielectric layer thickness yields heightened sensitivity and capacitance values, which is consistent with theoretical efforts. At a pressure of 200 kPa, the sensor achieves a maximum capacitance of 33.3 pF, with a total stored electric energy of 15.9 × 10-12 J and 0.468 pF/Pa of sensitivity for 0.1 dielectric thickness. These findings underscore the efficacy of the proposed CPS model for integration into capacitive touch buttons in electronic devices and e-skin applications, thereby offering promising advancements in sensor technology.

Rapid photocatalytic dye degradation, enhanced antibacterial and antifungal activities of silver stacked zinc oxide garnished on carbon nanotubes.

A composite of Zinc oxide loaded with 5-weight % silver decorated on carbon nanotubes (Ag-loaded ZnO: CNT) was synthesized using a simple refluxed chemical method. The influence of deviation in the weight % of carbon nanotube loading on photocatalytic dye degradation (methylene blue and rose bengal) and antibiotic (antimicrobial and antifungal) performance was investigated in this study. The light capture ability of Ag-loaded ZnO:CNT in the visible region was higher in photocatalytic activity than that of Ag-loaded ZnO and ZnO:CNT. The bandgap of the Ag-loaded ZnO: CNT was tuned owing to the surface plasmon resonance effect. The photocatalytic degradation investigations were optimized by varying the wt% in CNTs, pH of dye solution, concentration of the dye solution, and amount of catalytic dose. Around 100% photocatalytic efficiency in 2 min against MB dye was observed for Ag doped ZnO with 10 wt% CNT composite at pH 9, at a rate constant 1.48 min-1. Bipolaris sorokiniana fungus was first time tested against a composite material, which demonstrated optimum fungal inhibition efficiency of 48%. They were also tested against the bacterial strains Staphylococcus aureus, Bacillus cerius, Proteus vulgaris, and Salmonella typhimurium, which showed promising antibacterial activity compared to commercially available drugs. The composite of Ag doped ZnO with 5 wt% CNT has shown competitive zone inhibition efficacy of 21.66 ± 0.57, 15.66 ± 0.57, 13.66 ± 0.57 against bacterial strains Bacillus cerius, Proteus vulgaris, and Salmonella typhimurium which were tested for the first time against Ag-loaded ZnO:CNT.

Being all-seeing gymnast within kidney cavity: analysis of the optical and flexibility characteristics trends of 61 flexible ureteroscopes over four decades.

The purpose of this review is to analyze the trend in optical features and flexibility changes of flexible ureteroscopes over the past decades, and determine the correlation of individual parameters with release period as well as with dimensional parameters. Flexible ureteroscopes mentioned in the literature or those commercially available were searched. To minimize the search bias, the instruments were grouped by release date time-periods of < 2000 year, 2000-2009, 2010-2019, and 2020 onwards. The final review included only those instrument models for which data on minimum and maximum depth of field, field of view, direction of view, and deflection degree had been determined. The correlation among features investigated as well as with release period was also determined. 61 models of flexible ureteroscopes (27 fibreoptic and 34 digital scopes) were included. Among the different features investigated among fiberoptic endoscopes, minimum depth of field positively and negatively correlated with channel size and field of view, respectively, whereas maximum depth of view and field of view positively correlated with overall shaft and deflection degree, respectively. Up and down deflection strongly correlated with each other and both were negatively proportional to the distal tip size. For the digital endoscopes, minimum depth of field negatively and positively correlated with distal tip size and working length, respectively. Maximum depth of field positively correlated with field of view, whereas the latter was negatively proportional to the overall shaft. As for the fiberoptic counterparts, up and down deflection strongly correlated with each other. Field of view, up and down deflection of fiberoptic flexible ureteroscopes, were significantly increased among fiberoptic and digital endoscopes over decades. As flexible ureteroscopy technology has evolved, there has been a trend towards increasing field of view with up and down deflection. Given the importance of scope ergonomics, one aspect of this popularity is the improvement of optical characteristics and deflection degree, which significantly correlates with the release period.

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.

Catalyzing Precision Medicine: Artificial Intelligence Advancements in Prostate Cancer Diagnosis and Management.

BACKGROUND: The aim was to analyze the current state of deep learning (DL)-based prostate cancer (PCa) diagnosis with a focus on magnetic resonance (MR) prostate reconstruction; PCa detection/stratification/reconstruction; positron emission tomography/computed tomography (PET/CT); androgen deprivation therapy (ADT); prostate biopsy; associated challenges and their clinical implications. METHODS: A search of the PubMed database was conducted based on the inclusion and exclusion criteria for the use of DL methods within the abovementioned areas. RESULTS: A total of 784 articles were found, of which, 64 were included. Reconstruction of the prostate, the detection and stratification of prostate cancer, the reconstruction of prostate cancer, and diagnosis on PET/CT, ADT, and biopsy were analyzed in 21, 22, 6, 7, 2, and 6 studies, respectively. Among studies describing DL use for MR-based purposes, datasets with magnetic field power of 3 T, 1.5 T, and 3/1.5 T were used in 18/19/5, 0/1/0, and 3/2/1 studies, respectively, of 6/7 studies analyzing DL for PET/CT diagnosis which used data from a single institution. Among the radiotracers, [68Ga]Ga-PSMA-11, [18F]DCFPyl, and [18F]PSMA-1007 were used in 5, 1, and 1 study, respectively. Only two studies that analyzed DL in the context of DT met the inclusion criteria. Both were performed with a single-institution dataset with only manual labeling of training data. Three studies, each analyzing DL for prostate biopsy, were performed with single- and multi-institutional datasets. TeUS, TRUS, and MRI were used as input modalities in two, three, and one study, respectively. CONCLUSION: DL models in prostate cancer diagnosis show promise but are not yet ready for clinical use due to variability in methods, labels, and evaluation criteria. Conducting additional research while acknowledging all the limitations outlined is crucial for reinforcing the utility and effectiveness of DL-based models in clinical settings.

Role of virtual reality in improving the spatial perception of the kidney during flexible ureteroscopy: A feasibility study using virtual reality simulators and 3D models.

Background: The aims were to describe a software-based reconstruction of the patient-specific kidney cavity intraluminal appearance via a head-mounted device and to estimate its feasibility for training novices. Materials and methods: In total, 15 novices were recruited. Each novice was shown a three-dimensional reconstruction of a patient's computed tomography scan, whose kidney was printed. They then joined the surgeon in the operating room and assisted them in detecting the stone during flexible ureteroscopy on the printed model. Then, each participant did a 7-day virtual reality (VR) study followed by virtual navigation of the printed kidney model and came to the operating room to help the surgeon with ureteroscope navigation. The length of the procedure and the number of attempts to find the targeted calyx were compared. Results: With VR training, the length of the procedure (p = 0.0001) and the number of small calyces that were incorrectly identified as containing stones were significantly reduced (p = 0.0001). All the novices become highly motivated to improve their endourological skills further. Participants noticed minimal values for nausea and for disorientation. However, oculomotor-related side effects were defined as significant. Five specialists noticed a good similarity between the VR kidney cavity representation and the real picture, strengthening the potential for the novice's education via VR training. Conclusions: Virtual reality simulation allowed for improved spatial orientation within the kidney cavity by the novices and could be a valuable option for future endourological training and curricula.

Percutaneous Nephrolithotomy for De Novo Urolithiasis After Kidney Transplantation: A Systematic Review of the Literature.

Introduction and Objective: Renal transplantation is the treatment for end-stage renal disease that offers better quality of life and survival. Among the possible complications that might affect allografts, urolithiasis might have severe consequences, causing acute kidney injury (AKI) or septic events in immunocompromised patients. Allograft stones might be treated with percutaneous nephrolithotomy (PCNL). The aim of this Cochrane style review was to assess the safety and efficacy of PCNL in patients with renal transplant. Methods: A comprehensive search in the literature was performed including articles between July 1982 and June 2023, with only English original articles selected for this review. Results: The final review encompassed nine articles (108 patients). The mean age was 46.4 ± 8.7 years, with a male:female ratio of 54:44. The average time from transplantation to urolithiasis onset was 47.54 ± 23.9 months. Predominant symptoms upon presentation were AKI (32.3%), followed by urinary tract infection and fever (24.2%), and oliguria (12.9%). The mean stone size was 20.1 ± 7.3 mm, with stones located in the calices or pelvis (41%), ureteropelvic junction (23.1%), or proximal ureter (28.2%). PCNL (22F-30F) was more frequently performed than mini-PCNLs (16F-20F) (52.4% vs 47.6%). Puncture was guided by ultrasound (42.9%), fluoroscopy (14.3%), or both (42.9%). The stone-free rate (SFR) and complication rates were 92.95% (range: 77%-100%) and 5.5%, respectively, with only one major complication reported. Postoperatively, a ureteral stent and nephrostomy were commonly placed in 47%, with four patients needing a second look PCNL. During an average follow-up of 32.5 months, the recurrence rate was 3.7% (4/108), and the mean creatinine level was 1.37 ± 0.28 mg/dL. Conclusions: PCNL remains a safe and effective option in de novo allograft urolithiasis, allowing to treat large stones in one-step surgery. A good SFR is achieved with a low risk of minor complications. These patients should be treated in an endourology center in conjunction with the renal or transplant team.

Potential of AI-Driven Chatbots in Urology: Revolutionizing Patient Care Through Artificial Intelligence.

PURPOSE OF REVIEW: Artificial intelligence (AI) chatbots have emerged as a potential tool to transform urology by improving patient care and physician efficiency. With an emphasis on their potential advantages and drawbacks, this literature review offers a thorough assessment of the state of AI-driven chatbots in urology today. RECENT FINDINGS: The capacity of AI-driven chatbots in urology to give patients individualized and timely medical advice is one of its key advantages. Chatbots can help patients prioritize their symptoms and give advice on the best course of treatment. By automating administrative duties and offering clinical decision support, chatbots can also help healthcare providers. Before chatbots are widely used in urology, there are a few issues that need to be resolved. The precision of chatbot diagnoses and recommendations might be impacted by technical constraints like system errors and flaws. Additionally, issues regarding the security and privacy of patient data must be resolved, and chatbots must adhere to all applicable laws. Important issues that must be addressed include accuracy and dependability because any mistakes or inaccuracies could seriously harm patients. The final obstacle is resistance from patients and healthcare professionals who are hesitant to use new technology or who value in-person encounters. AI-driven chatbots have the potential to significantly improve urology care and efficiency. However, it is essential to thoroughly test and ensure the accuracy of chatbots, address privacy and security concerns, and design user-friendly chatbots that can integrate into existing workflows. By exploring various scenarios and examining the current literature, this review provides an analysis of the prospects and limitations of implementing chatbots in urology.

Exploring the Ethical, Legal, and Social Implications of ChatGPT in Urology.

PURPOSE OF THE REVIEW: ChatGPT is programmed to generate responses based on pattern recognition. With this vast popularity and exponential growth, the question arises of moral issues, security and legitimacy. In this review article, we aim to analyze the ethical and legal implications of using ChatGPT in Urology and explore potential solutions addressing these concerns. RECENT FINDINGS: There are many potential applications of ChatGPT in urology, and the extent to which it might improve healthcare may cause a profound shift in the way we deliver our services to patients and the overall healthcare system. This encompasses diagnosis and treatment planning, clinical workflow, patient education, augmenting consultations, and urological research. The ethical and legal considerations include patient autonomy and informed consent, privacy and confidentiality, bias and fairness, human oversight and accountability, trust and transparency, liability and malpractice, intellectual property rights, and regulatory framework. The application of ChatGPT in urology has shown great potential to improve patient care and assist urologists in various aspects of clinical practice, research, and education. Complying with data security and privacy regulations, and ensuring human oversight and accountability are some potential solutions to these legal and ethical concerns. Overall, the benefits and risks of using ChatGPT in urology must be weighed carefully, and a cautious approach must be taken to ensure that its use aligns with human values and advances patient care ethically and responsibly.

Digital health innovations in urology: telemedicine, wearables, and mobile applications - a systematic review of literature.

PURPOSE OF REVIEW: There are enough publications on the use of telemedicine, wearable devices, and mobile applications in urology; however, their collective impact on urological care has not been adequately studied. This review seeks to address this deficiency by providing a descriptive analysis of the recent use of telemedicine, wearable technology, and mobile applications in urology as well as elucidating their associated challenges. RECENT FINDINGS: There are studies that were dedicated to the use of telemedicine, wearables, and mobile apps in urology according to inclusion criteria, respectively. They were successfully implemented in different urological subfields, such as urogynecology, endourology, pediatric urology, and uro-oncology, and led to time safety, remote monitoring, and better patient awareness. However, several concerns also exist, such as issues with data safety, measurement deviations, technical limitations, and lack ofquality. SUMMARY: Telemedicine, wearables, and mobile apps have already shown their potential in urological practice. However, further studies are needed to expand both our understanding of their current state and their potential for further development and clinical use.

Smart Diagnosis of Urinary Tract Infections: is Artificial Intelligence the Fast-Lane Solution?

PURPOSE OF REVIEW: Artificial intelligence (AI) can significantly improve physicians' workflow when examining patients with UTI. However, most contemporary reviews are focused on examining the usage of AI with a restricted quantity of data, analyzing only a subset of AI algorithms, or performing narrative work without analyzing all dedicated studies. Given the preceding, the goal of this work was to conduct a mini-review to determine the current state of AI-based systems as a support in UTI diagnosis. RECENT FINDINGS: There are sufficient publications to comprehend the potential applications of artificial intelligence in the diagnosis of UTIs. Existing research in this field, in general, publishes performance metrics that are exemplary. However, upon closer inspection, many of the available publications are burdened with flaws associated with the improper use of artificial intelligence, such as the use of a small number of samples, their lack of heterogeneity, and the absence of external validation. AI-based models cannot be classified as full-fledged physician assistants in diagnosing UTIs due to the fact that these limitations and flaws represent only a portion of all potential obstacles. Instead, such studies should be evaluated as exploratory, with a focus on the importance of future work that complies with all rules governing the use of AI. AI algorithms have demonstrated their potential for UTI diagnosis. However, further studies utilizing large, heterogeneous, prospectively collected datasets, as well as external validations, are required to define the actual clinical workflow value of artificial intelligence.

Miniaturization of flexible ureteroscopes: a comparative trend analysis of 59 flexible ureteroscopes.

The purpose of this review is to analyze the trend in miniaturization of flexible ureteroscopes over the past decades, identify the advantages and disadvantages, and determine the correlation of individual parameters with release period. Flexible ureteroscopes mentioned in the literature or those commercially available were searched. To minimize the search bias, the instruments were grouped by release date time periods of < 2000 year, 2000-2009, 2010-2019, and 2020 onwards. The final review included only those instrument models for which data on tip size, overall shaft, working length and channel size had been determined. The correlation among features investigated as well as with release period was also determined. 59 models of flexible ureteroscopes (26 fiber optic and 33 digital scopes) were included. Among the different features investigated among fiber optic endoscopes, only the sizes of the distal tip and overall shaft positively correlated with each other. In contrast to their fiber optic counterparts, a strong positive correlation was observed between tip and channel sizes, whereas negative correlation was found between channel size and overall shaft size and working length of digital scopes. Only distal tip of fiber optic flexible ureteroscopes and overall shaft of digital endoscopes were significantly reduced over their evolution. With the development of technology, there has been an improvement of flexible ureteroscopes and one of the indicators of this trend is a decrease in their size. With a definite trend towards miniaturization over the past decades, a significant correlation was observed in tip size and overall shaft for fiber optic and digital endoscopes, respectively.

A Nonlinear Three-Dimensional Finite Element Analysis of Stress Distribution and Microstrain Evaluation in Short Dental Implants with Three Different Implant-Abutment Connections in Single and Splinted Conditions in the Posterior Mandible.

Background: Stress distribution plays a vital role in the longevity and success of implant-supported prosthesis. This study evaluated the von Mises stress and microstrain in the peri-implant bone and the implant-abutment junction of short dental implants with three different implant-abutment connections in splinted and unsplinted conditions using finite element analysis (FEA). Materials and Methods: In this experimental study, nine transversely isotropic finite element models were developed, and randomly divided into three equal groups (n = 3): control, (Group AC) single-standard 4.3 × 10 mm bone level implant-supported restorations with external hexagonal (EH) connection, internal conical (IC) and internal trichannel (ITC) connection, single short implant-supported restorations (Group AT), and splinted short implant-supported restorations (Group B) for each of the three implant-abutment connections, respectively. A 200 N load was applied along the long axis of the implants and a 100 N (45°) oblique load was applied and von Mises stress and microstrain values were evaluated. Results: Single standard implants demonstrated the highest von Mises stress and microstrain values followed by single short implants and splinted short implants, respectively. Among the implant-abutment connections, the IC connection showed the highest values and the ITC connection showed the least values. Conclusion: Within the limitations of this study, it was concluded that splinting of short dental implants demonstrated lesser and more homogeneous stress and microstrain, especially on oblique loading. The microstrain values for all connections evaluated were within the physiological loading limit (200-2,500 N) and were hence considered safe for clinical use.

Expanding horizons and navigating challenges for enhanced clinical workflows: ChatGPT in urology.

Purpose of review: ChatGPT has emerged as a potential tool for facilitating doctors' workflows. However, when it comes to applying these findings within a urological context, there have not been many studies. Thus, our objective was rooted in analyzing the pros and cons of ChatGPT use and how it can be exploited and used by urologists. Recent findings: ChatGPT can facilitate clinical documentation and note-taking, patient communication and support, medical education, and research. In urology, it was proven that ChatGPT has the potential as a virtual healthcare aide for benign prostatic hyperplasia, an educational and prevention tool on prostate cancer, educational support for urological residents, and as an assistant in writing urological papers and academic work. However, several concerns about its exploitation are presented, such as lack of web crawling, risk of accidental plagiarism, and concerns about patients-data privacy. Summary: The existing limitations mediate the need for further improvement of ChatGPT, such as ensuring the privacy of patient data and expanding the learning dataset to include medical databases, and developing guidance on its appropriate use. Urologists can also help by conducting studies to determine the effectiveness of ChatGPT in urology in clinical scenarios and nosologies other than those previously listed.

Current state of mobile health apps in endourology: a review of mobile platforms in marketplaces and literature.

Several mobile healthcare (mHealth) apps are available in various marketplaces, but there is still concern about their accuracy, data safety, and regulation. The goal of this review was to critically analyze the mobile apps created for education, diagnosis, and medical and surgical treatment of patients with kidney stone disease (KSD), as well as to assess the level of data security, the contribution of physicians in their development and adherence to the Food and Drug Administration (FDA) and Medical Device Regulation (MDR) guidance. A comprehensive literature search was performed using PubMed (September 2022), in the Apple App Store and Google Play store using relevant keywords and inclusion criteria. Information was extracted for the name of the app, primary and additional functionalities, release and last update, number of downloads, number of marks and average rating, Android/iOS compatibility, initial and in-app payments, data safety statement, physician involvement statement, and FDA/MDR guidance. A total of 986 apps and 222 articles were reviewed, of which based on the inclusion, 83 apps were finally analyzed. The apps were allocated to six categories about their primary purpose: education (n = 8), fluid trackers (n = 54), food content description and calculators (n = 11), diagnosis (n = 3), pre- and intra-operative application (n = 4), and stent trackers (n = 2). Of these apps, the number of apps supported for Android, iOS, and both of them were 36, 23, and 23, respectively. Despite a wide range of apps available for KSD, the participation of doctors in their development, data security, and functionality remains insufficient. Further development of mHealth should be carried out properly under the supervision of urological associations involving patient support groups, and these apps must be regularly updated for their content and data security.

Radiomics vs radiologist in bladder and renal cancer. Results from a systematic review.

Introduction: Radiomics in uro-oncology is a rapidly evolving science proving to be a novel approach for optimizing the analysis of massive data from medical images to provide auxiliary guidance in clinical issues. This scoping review aimed to identify key aspects wherein radiomics can potentially improve the accuracy of diagnosis, staging, and grading of renal and bladder cancer. Material and methods: A literature search was performed in June 2022 using PubMed, Embase, and Cochrane Central Controlled Register of Trials. Studies were included if radiomics were compared with radiological reports only. Results: Twenty-two papers were included, 4 were pertinent to bladder cancer, and 18 to renal cancer. Radiomics outperforms the visual assessment by radiologists in contrast-enhanced computed tomography (CECT) to predict muscle invasion but are equivalent to CT reporting by radiologists in predicting lymph node metastasis. Magnetic resonance imaging (MRI) radiomics outperforms radiological reporting for lymph node metastasis. Radiomics perform better than radiologists reporting the probability of renal cell carcinoma, improving interreader concordance and performance. Radiomics also helps to determine differences in types of renal pathology and between malignant lesions from their benign counterparts. Radiomics can be helpful to establish a model for differentiating low-grade from high-grade clear cell renal cancer with high accuracy just from contrast-enhanced CT scans. Conclusions: Our review shows that radiomic models outperform individual reports by radiologists by their ability to incorporate many more complex radiological features.