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.

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.

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.

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.

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.

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.

Radiomics vs radiologist in prostate cancer. Results from a systematic review.

PURPOSE: 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 extraprostatic extension in prostate cancer (PCa). METHODS: The literature search was performed on June 2022 using PubMed, Embase, and Cochrane Central Controlled Register of Trials. Studies were included if radiomics were compared with radiological reports only. RESULTS: Seventeen papers were included. The combination of PIRADS and radiomics score models improves the PIRADS score reporting of 2 and 3 lesions even in the peripheral zone. Multiparametric MRI-based radiomics models suggest that by simply omitting diffusion contrast enhancement imaging in radiomics models can simplify the process of analysis of clinically significant PCa by PIRADS. Radiomics features correlated with the Gleason grade with excellent discriminative ability. Radiomics has higher accuracy in predicting not only the presence but also the side of extraprostatic extension. CONCLUSIONS: Radiomics research on PCa mainly uses MRI as an imaging modality and is focused on diagnosis and risk stratification and has the best future possibility of improving PIRADS reporting. Radiomics has established its superiority over radiologist-reported outcomes but the variability has to be taken into consideration before translating it to clinical practice.

Role of three dimensional (3D) printing in endourology: An update from EAU young academic urologists (YAU) urolithiasis and endourology working group.

The management of nephrolithiasis has been complemented well by modern technological advancements like virtual reality, three-dimensional (3D) printing etc. In this review, we discuss the applications of 3D printing in treating stone disease using percutaneous nephrolithotomy (PCNL) and retrograde intrarenal surgery (RIRS). PCNL surgeries, when preceded by a training phase using a 3D printed model, aid surgeons to choose the proper course of action, which results in better procedural outcomes. The 3D printed models have also been extensively used to train junior residents and novice surgeons to improve their proficiency in the procedure. Such novel measures include different approaches employed to 3D print a model, from 3D printing the entire pelvicalyceal system with the surrounding tissues to 3D printing simple surgical guides.

Role of Deep Learning in Prostate Cancer Management: Past, Present and Future Based on a Comprehensive Literature Review.

This review aims to present the applications of deep learning (DL) in prostate cancer diagnosis and treatment. Computer vision is becoming an increasingly large part of our daily lives due to advancements in technology. These advancements in computational power have allowed more extensive and more complex DL models to be trained on large datasets. Urologists have found these technologies help them in their work, and many such models have been developed to aid in the identification, treatment and surgical practices in prostate cancer. This review will present a systematic outline and summary of these deep learning models and technologies used for prostate cancer management. A literature search was carried out for English language articles over the last two decades from 2000-2021, and present in Scopus, MEDLINE, Clinicaltrials.gov, Science Direct, Web of Science and Google Scholar. A total of 224 articles were identified on the initial search. After screening, 64 articles were identified as related to applications in urology, from which 24 articles were identified to be solely related to the diagnosis and treatment of prostate cancer. The constant improvement in DL models should drive more research focusing on deep learning applications. The focus should be on improving models to the stage where they are ready to be implemented in clinical practice. Future research should prioritize developing models that can train on encrypted images, allowing increased data sharing and accessibility.

Telemedicine and Telehealth in Urology: Uptake, Impact and Barriers to Clinical Adoption.

Telemedicine has great potential in urology as a strong medium for providing patients with continuous high-quality urological care despite the hurdles involved in its implementation. Both clinicians and patients are crucial factors in determining the success of tele-consults in terms of simplicity of use and overall satisfaction. For it to be successfully incorporated into routine urological practice, rigorous training and evidence-based recommendations are lacking. If these issues are addressed, they can provide a significant impetus for future tele-consults in urology and their successful deployment, even beyond the pandemic, to assure safer and more environment-friendly patient management.

Telemedicine and Telehealth in Urology-What Do the 'Patients' Think About It?

Telemedicine is the delivery of healthcare to patients who are not in the same location as the physician. The practice of telemedicine has a large number of advantages, including cost savings, low chances of nosocomial infection, and fewer hospital visits. Teleclinics have been reported to be successful in the post-surgery and post-cancer therapy follow-up, and in offering consulting services for urolithiasis patients. This review focuses on identifying the outcomes of the recent studies related to the usage of video consulting in urology centers for hematuria referrals and follow-up appointments for a variety of illnesses, including benign prostatic hyperplasia (BPH), kidney stone disease (KSD), and urinary tract infections (UTIs) and found that they are highly acceptable and satisfied. Certain medical disorders can cause embarrassment, social exclusion, and also poor self-esteem, all of which can negatively impair health-related quality-of-life. Telemedicine has proven beneficial in such patients and is a reliable, cost-effective patient-care tool, and it has been successfully implemented in various healthcare settings and specialties.

Application of Virtual Reality, Augmented Reality, and Mixed Reality in Endourology and Urolithiasis: An Update by YAU Endourology and Urolithiasis Working Group.

The integration of virtual reality (VR), augmented reality (AR), and mixed reality (MR) in urological practices and medical education has led to modern training systems that are cost-effective and with an increased expectation toward surgical performance and outcomes. VR aids the user in interacting with the virtual environment realistically by providing a three-dimensional (3D) view of the structures inside the body with high-level precision. AR enhances the real environment around users by integrating experience with virtual information over physical models and objects, which in turn has improved understanding of physiological mechanisms and anatomical structures. MR is an immersive technology that provides virtual content to interact with real elements. The field of urolithiasis has adapted the technological advancements, newer instruments, and methods to perform endourologic treatment procedures. This mini-review discusses the applications of Virtual Reality, Augmented Reality, and Mixed Reality in endourology and urolithiasis.

Legal and Ethical Consideration in Artificial Intelligence in Healthcare: Who Takes Responsibility?

The legal and ethical issues that confront society due to Artificial Intelligence (AI) include privacy and surveillance, bias or discrimination, and potentially the philosophical challenge is the role of human judgment. Concerns about newer digital technologies becoming a new source of inaccuracy and data breaches have arisen as a result of its use. Mistakes in the procedure or protocol in the field of healthcare can have devastating consequences for the patient who is the victim of the error. Because patients come into contact with physicians at moments in their lives when they are most vulnerable, it is crucial to remember this. Currently, there are no well-defined regulations in place to address the legal and ethical issues that may arise due to the use of artificial intelligence in healthcare settings. This review attempts to address these pertinent issues highlighting the need for algorithmic transparency, privacy, and protection of all the beneficiaries involved and cybersecurity of associated vulnerabilities.

A Feature Extraction Using Probabilistic Neural Network and BTFSC-Net Model with Deep Learning for Brain Tumor Classification.

BACKGROUND AND OBJECTIVES: Brain Tumor Fusion-based Segments and Classification-Non-enhancing tumor (BTFSC-Net) is a hybrid system for classifying brain tumors that combine medical image fusion, segmentation, feature extraction, and classification procedures. MATERIALS AND METHODS: to reduce noise from medical images, the hybrid probabilistic wiener filter (HPWF) is first applied as a preprocessing step. Then, to combine robust edge analysis (REA) properties in magnetic resonance imaging (MRI) and computed tomography (CT) medical images, a fusion network based on deep learning convolutional neural networks (DLCNN) is developed. Here, the brain images' slopes and borders are detected using REA. To separate the sick region from the color image, adaptive fuzzy c-means integrated k-means (HFCMIK) clustering is then implemented. To extract hybrid features from the fused image, low-level features based on the redundant discrete wavelet transform (RDWT), empirical color features, and texture characteristics based on the gray-level cooccurrence matrix (GLCM) are also used. Finally, to distinguish between benign and malignant tumors, a deep learning probabilistic neural network (DLPNN) is deployed. RESULTS: according to the findings, the suggested BTFSC-Net model performed better than more traditional preprocessing, fusion, segmentation, and classification techniques. Additionally, 99.21% segmentation accuracy and 99.46% classification accuracy were reached using the proposed BTFSC-Net model. CONCLUSIONS: earlier approaches have not performed as well as our presented method for image fusion, segmentation, feature extraction, classification operations, and brain tumor classification. These results illustrate that the designed approach performed more effectively in terms of enhanced quantitative evaluation with better accuracy as well as visual performance.

Artificial Intelligence and Its Impact on Urological Diseases and Management: A Comprehensive Review of the Literature.

Recent advances in artificial intelligence (AI) have certainly had a significant impact on the healthcare industry. In urology, AI has been widely adopted to deal with numerous disorders, irrespective of their severity, extending from conditions such as benign prostate hyperplasia to critical illnesses such as urothelial and prostate cancer. In this article, we aim to discuss how algorithms and techniques of artificial intelligence are equipped in the field of urology to detect, treat, and estimate the outcomes of urological diseases. Furthermore, we explain the advantages that come from using AI over any existing traditional methods.

Big Data Analytics in urology: the story so far and the road ahead.

Artificial intelligence (AI) has a proven record of application in the field of medicine and is used in various urological conditions such as oncology, urolithiasis, paediatric urology, urogynaecology, infertility and reconstruction. Data is the driving force of AI and the past decades have undoubtedly witnessed an upsurge in healthcare data. Urology is a specialty that has always been at the forefront of innovation and research and has rapidly embraced technologies to improve patient outcomes and experience. Advancements made in Big Data Analytics raised the expectations about the future of urology. This review aims to investigate the role of big data and its blend with AI for trends and use in urology. We explore the different sources of big data in urology and explicate their current and future applications. A positive trend has been exhibited by the advent and implementation of AI in urology with data available from several databases. The extensive use of big data for the diagnosis and treatment of urological disorders is still in its early stage and under validation. In future however, big data will no doubt play a major role in the management of urological conditions.

Synergistic effect of carboxymethylcellulose and Cryptococcus laurentii on suppressing green mould of postharvest grapefruit and its mechanism.

The synergistic effects of carboxymethylcellulose (CMC) combined with Cryptococcus laurentii FRUC DJ1 were studied on controlling green mould resulting from Penicillium digitatum in grapefruit fruit. The results indicate that both C. laurentii and the CMC treatment suppressed P. digitatum conidia germination. In addition, C. laurentii growth in vitro was not affected by low CMC concentrations, nevertheless, the biofilm of C. laurentii was enhanced. Compared with the control fruit, the grapefruit had a lower green mould in all treatments. Significantly synergistic effects were caused by combining C. laurentii and CMC on minimum decay incidence and lesion diameter. Combined treatment induced defence enzyme activities, including chitinase, ß-1,3-glucanase, peroxidase, polyphenol oxidase, and phenylalanine ammonia-lyase, together with disease tolerance-associated total phenol. Also, this combination inhibited the pathogen growth by adhered to the hyphae and reduced its infection in fruit wounds. Moreover, the commercial quality parameters in the combined treatment of C. laurentii and CMC, including weight loss, total soluble solids, ascorbic acid, and titratable acidity, were superior to single treatment. The combination of C. laurentii and CMC can not only control postharvest decay but also maintain fruit qualities. Thus, it can be used in grapefruit for commercial purposes.