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.

This study explores the potential of the VGG-16 architecture, a Convolutional Neural Network (CNN) model, for accurate brain tumor detection through deep learning. Utilizing a dataset consisting of 1655 brain MRI images with tumors and 1598 images with- out tumors, the VGG-16 model was fine-tuned and trained on this data. Initial training achieved an accuracy of 91%, which was improved to 94% after hyperparameter optimization. The model’s sensitivity, specificity, precision, recall, and F1 scores were strong, indicating its potential in accurately detecting brain tumors. The performance of the VGG-16 model was compared to several other techniques for brain tumor detection, including EasyDL, GoogLeNet, GrayNet, ImageNet, CNN, and a Multivariable Regression and Neural Network model. Although it did not achieve the highest accuracy, it outperformed GoogLeNet and ImageNet and demon- strated comparable accuracy to GrayNet and the Multivariable Regression and Neural Network. Its sensitivity and specificity suggest its potential in identifying tumors that other methods might miss, reinforcing its potential usefulness in medical applications. Never- theless, there is room for improvement. Future studies could collect and annotate larger datasets to improve model generalizability. Exploring other deep learning architectures and enhancing model interpretability could further boost its clinical relevance. Despite these challenges, this study demonstrates the untapped potential of the VGG-16 architecture in brain tumor detection and contributes to the growing body of research on applying deep learning in the medical field.

Artificial intelligence (AI) is an emerging technological system that provides a platform to manage and analyze data by emulating human cognitive functions with greater accuracy, revolutionizing patient care and introducing a paradigm shift to the healthcare industry. The purpose of this study is to identify the underlying factors that affect the adoption of artificial intelligence in healthcare (AIH) by healthcare providers and to understand “What are the factors that influence healthcare providers’ behavioral intentions to adopt AIH in their routine practice?” An integrated survey was conducted among healthcare providers, including consultants, residents/students, and nurses. The survey included items related to performance expectancy, effort expectancy, initial trust, personal innovativeness, task complexity, and technology characteristics. The collected data were analyzed using structural equation modeling. A total of 392 healthcare professionals participated in the survey, with 72.4% being male and 50.7% being 30 years old or younger. The results showed that performance expectancy, effort expectancy, and initial trust have a positive influence on the behavioral intentions of healthcare providers to use AIH. Personal innovativeness was found to have a positive influence on effort expectancy, while task complexity and technology characteristics have a positive influence on effort expectancy for AIH. The study’s empirically validated model sheds light on healthcare providers’ intention to adopt AIH, while the study’s findings can be used to develop strategies to encourage this adoption. However, further investigation is necessary to understand the individual factors affecting the adoption of AIH by healthcare providers.

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.

BACKGROUND AND OBJECTIVES: Enhanced recovery after surgery (ERAS) protocols aim to optimize patient outcomes by reducing the surgical stress response, expediting recovery, and reducing care costs. We aimed to evaluate the impact of implementing ERAS protocols on the perioperative surgical outcomes and financial implications associated with spine surgeries. METHODS: A systematic review and meta-analysis of peer-reviewed studies directly comparing outcome differences between spine surgeries performed with and without utilization of ERAS pathways was conducted along Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. RESULTS: Of 676 unique articles identified, 59 with 15 198 aggregate patients (7748 ERAS; 7450 non-ERAS) were included. ERAS-treated patients had shorter operative times (mean difference [MD]: 10.2 mins; P < .01), shorter hospitalizations (MD: 1.41 days, P < .01), fewer perioperative complications (relative risk [RR] = 0.64, P < .01), lower postoperative opioid use (MD of morphine equivalent dose: 164.36 mg; P < .01), and more rapid mobilization/time to first out-of-bed ambulation (MD: 0.92 days; P < .01). Spine surgeries employing ERAS were also associated with lower total costs (MD: $1140.26/patient; P < .01), especially in the United States (MD: $2869.11/patient, P < .01) and lower postoperative visual analog pain scores (MD = 0.56, P < .01), without any change in odds of 30-day readmission (RR: 0.80, P = .13) or reoperation (RR: 0.88, P = .60). Subanalyses based on the region of spine showed significantly lower length of stay in both cervical and lumbar surgeries implementing ERAS. Type of procedure showed a significantly lesser time-to-initiate mobilization in fusion surgeries using ERAS protocols compared with decompression. CONCLUSION: The present meta-analysis indicates that current literature supports ERAS implementation as a means of reducing care costs and safely accelerating hospital discharge for patients undergoing spine surgery.

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.