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n or equal to two comorbidities among the multisystem inflammatory syndrome in children cohort. Among nonmultisystem inflammatory syndrome in children patients, the presence of greater than or equal to two comorbidities was associated with greater odds of critical illness (odds ratio 2.95 [95% CI, 1.61-5.40]; p < 0.01).

This study delineates significant clinically relevant differences in presentation, explanatory factors, and outcomes among children admitted to PICU with severe acute respiratory syndrome coronavirus 2-related illness stratified by multisystem inflammatory syndrome in children.

This study delineates significant clinically relevant differences in presentation, explanatory factors, and outcomes among children admitted to PICU with severe acute respiratory syndrome coronavirus 2-related illness stratified by multisystem inflammatory syndrome in children.

Artificial intelligence (AI) is the ability of a machine, or computer, to simulate intelligent behavior. In medicine, the use of large datasets enables a computer to learn how to perform cognitive tasks, thereby facilitating medical decision-making. This review aims to describe advancements in AI in stone disease to improve diagnostic accuracy in determining stone composition, to predict outcomes of surgical procedures or watchful waiting and ultimately to optimize treatment choices for patients.

AI algorithms show high accuracy in different realms including stone detection and in the prediction of surgical outcomes. There are machine learning algorithms for outcomes after percutaneous nephrolithotomy, extracorporeal shockwave lithotripsy, and for ureteral stone passage. Some of these algorithms show better predictive capabilities compared to existing scoring systems and nomograms.

The use of AI can facilitate the development of diagnostic and treatment algorithms in patients with stone disease. Although the generalizability and external validity of these algorithms remain uncertain, the development of highly accurate AI-based tools may enable the urologist to provide more customized patient care and superior outcomes.

The use of AI can facilitate the development of diagnostic and treatment algorithms in patients with stone disease. Although the generalizability and external validity of these algorithms remain uncertain, the development of highly accurate AI-based tools may enable the urologist to provide more customized patient care and superior outcomes.

As technology advances, surgical training has evolved in parallel over the previous decade. Training is commonly seen as a way to prepare surgeons for their day-to-day work; however, more importantly, it allows for certification of skills to ensure maximum patient safety. Alpelisib chemical structure This article reviews advances in the use of machine learning and artificial intelligence for improvements of surgical skills in urology.

Six studies have been published, which met the inclusion criteria. All articles assessed the application of artificial intelligence in improving surgical training. Different approaches were taken, such as using machine learning to identify and classify suturing gestures, creating automated objective evaluation reports, and determining surgical technical skill levels to predict clinical outcomes. The articles illustrated the continuously growing role of artificial intelligence to address the difficulties currently present in evaluating urological surgical skills.

Artificial intelligence allows us to efurgeon feedback.

The purpose of this review was to identify the most recent lines of research focusing on the application of artificial intelligence (AI) in the diagnosis and staging of prostate cancer (PCa) with imaging.

The majority of studies focused on the improvement in the interpretation of bi-parametric and multiparametric magnetic resonance imaging, and in the planning of image guided biopsy. These initial studies showed that AI methods based on convolutional neural networks could achieve a diagnostic performance close to that of radiologists. In addition, these methods could improve segmentation and reduce inter-reader variability. Methods based on both clinical and imaging findings could help in the identification of high-grade PCa and more aggressive disease, thus guiding treatment decisions. Though these initial results are promising, only few studies addressed the repeatability and reproducibility of the investigated AI tools. Further, large-scale validation studies are missing and no diagnostic phase III or higher studies proving improved outcomes regarding clinical decision making have been conducted.

AI techniques have the potential to significantly improve and simplify diagnosis, risk stratification and staging of PCa. Larger studies with a focus on quality standards are needed to allow a widespread introduction of AI in clinical practice.

AI techniques have the potential to significantly improve and simplify diagnosis, risk stratification and staging of PCa. Larger studies with a focus on quality standards are needed to allow a widespread introduction of AI in clinical practice.

Sarcopenia is known to affect perioperative and oncologic outcomes in patients with different urological malignancies. Nevertheless, the use of pretreatment sarcopenia as a predictor of clinical outcomes in patients with prostate cancer is still poorly studied. Therefore, we aimed to conduct a systematic review summarizing the available evidence and identifying the prognostic value of sarcopenia in prostate cancer patients.

Sarcopenia was not predictive of biochemical recurrence in patients treated with radical prostatectomy. However, it was associated with worse long-term survival outcomes as well as the likelihood of developing postoperative complications after radical prostatectomy. In the context of radiotherapy, sarcopenia was a predictive factor for overall survival. In patients with hormone-sensitive prostate cancer treated with androgen deprivation, sarcopenia was associated with overall and cancer-specific survival. In patients with castration-resistant prostate cancer, sarcopenia was associated with poorer tolerance to docetaxel-based chemotherapy.