Despite rapid proliferation of AI applications in prostate MRI showing impressive technical performance, clinical adoption remains limited. We conducted a comprehensive narrative review of literature from January 2018 to December 2024, examining AI applications in prostate MRI with emphasis on real-world performance and implementation challenges. Among 200+ studies reviewed, AI systems achieve 87% sensitivity and 72% specificity for cancer detection in research settings. However, external validation reveals average performance drops of 12%, with some implementations showing degradation up to 31%. Only 31% of studies follow reporting guidelines, 11% share code, and 4% provide model weights. Seven real-world implementation studies demonstrate integration times of 3–14 months, with one major center terminating deployment due to unacceptable false positive rates. The translation gap between artificial and clinical intelligence remains substantial. Success requires shifting focus from accuracy metrics to patient outcomes, establishing transparent reporting standards, developing realistic economic models, and creating appropriate regulatory frameworks. The field must combine methodological rigor, clinical relevance, and implementation science to realize AI’s transformative potential in prostate cancer care.

The discovery of a resident urinary microbiome has significantly altered the understanding of urolithiasis, expanding its etiology beyond metabolic and dietary factors to include microbial contributions. This review highlights how specific uropathogens—particularly Proteus mirabilis, Klebsiella pneumoniae, and Escherichia coli—facilitate stone formation through mechanisms such as urease activity, citrate degradation, urine alkalinization, biofilm development, and inflammatory signaling. We critically examine how antibiotic therapies, while essential for treating urinary tract infections (UTIs), disrupt microbial homeostasis by depleting beneficial taxa like Lactobacillus and enabling colonization by lithogenic and resistant strains. Recurrent or broad-spectrum antibiotic use is linked to persistent dysbiosis and increased risk of stone recurrence. Additionally, this paper explores emerging microbiome-targeted strategies—such as probiotics, prebiotics, bacteriotherapy, and precision antimicrobials—as potential interventions to restore microbial balance and mitigate stone risk. Recognizing the urinary microbiome as a therapeutic target opens new avenues for personalized, microbiota-conscious strategies in the prevention and management of kidney stone disease.

Adrenal glands are vital endocrine organs whose accurate segmentation on CT imaging presents significant challenges due to their small size and variable morphology. This study evaluates the efficacy of deep learning approaches for automatic adrenal gland segmentation from multiphase CT scans. We implemented three convolutional neural network architectures (U-Net, SegNet, and NablaNet) and assessed their performance on a dataset comprising 868 adrenal glands from contrast-enhanced abdominal CT scans. Performance was evaluated using the Dice similarity coefficient (DSC), alongside practical implementation metrics including training and deployment time. U-Net demonstrated superior segmentation performance (DSC: 0.630 ± 0.05 for right, 0.660 ± 0.06 for left adrenal glands) compared to NablaNet (DSC: 0.552 ± 0.08 for right, 0.550 ± 0.07 for left) and SegNet (DSC: 0.320 ± 0.10 for right, 0.335 ± 0.09 for left). While all models achieved high specificity, boundary delineation accuracy remained challenging. Our findings demonstrate the feasibility of deep learning-based adrenal gland segmentation while highlighting the persistent challenges in achieving the segmentation quality observed with larger abdominal organs. U-Net provides the optimal balance between accuracy and computational requirements, establishing a foundation for further refinement of AI-assisted adrenal imaging tools.

Urinary tract infections (UTIs) sit at a leading edge of the antimicrobial-resistance (AMR) crisis, and Romania ranks among the most affected European countries, yet large single-center series of urinary isolates with complete antibiograms remain scarce. We describe the full resistance landscape of urinary pathogens at a Romanian tertiary hospital, with particular attention to multidrug-resistant Klebsiella and to the often-overlooked urease-positive organisms. We analyzed all positive urine cultures processed over 13 consecutive months (February 2025–February 2026). The first isolate per patient was the primary unit of analysis (n = 2331); the full isolate-level dataset (n = 3348) was analyzed secondarily. Resistance phenotypes (putative ESBL, carbapenem resistance, MDR, VRE) were derived from per-agent susceptibility data following Magiorakos et al., and resistance rates were calculated only on tested isolates. Associations were tested with chi-square/Fisher tests and Benjamini–Hochberg correction. A total of 3348 isolates from 2331 unique patients were analyzed (median age 68 years; 55% male). Escherichia coli predominated (40.5%), followed by Enterococcus spp. (22.2%) and Klebsiella spp. (18.9%). Among the first isolates, 15.3% were MDR; 16.7% of E. coli, Klebsiella and Proteus showed a putative ESBL phenotype; and 3.9% of Enterobacterales were carbapenem-resistant. Resistance was concentrated in Klebsiella (36.2% MDR, 22.8% putative ESBL, 14.7% carbapenem-resistant; all higher than E. coli, p < 0.001) and in Pseudomonas spp. (48.1% carbapenem-resistant). E. coli retained excellent activity to nitrofurantoin (1.2%), fosfomycin (1.2%; tested in a subset) and ertapenem (0.1%) despite high trimethoprim–sulfamethoxazole (31.5%) and fluoroquinolone (24.3%) resistance. Urease-positive organisms formed a distinct subgroup with high trimethoprim–sulfamethoxazole resistance but preserved carbapenem activity. The urinary resistance burden was substantial but uneven, concentrated in Klebsiella and Pseudomonas. These findings support a stratified, locally guided empirical strategy and combined stewardship–infection control efforts.

Background/Objectives: Autism Spectrum Disorder (ASD) is associated with structural brain alterations, particularly involving white matter and connectivity. Artificial intelligence (AI) enhances the detection of subtle neuroanatomical changes. This study aimed to characterize structural abnormalities and volumetric patterns in children with ASD using AI-assisted MRI. Methods: This retrospective study included 90 children diagnosed with ASD. Brain MRI scans were analyzed using the CE-certified AI platform mdbrain. Structural findings were classified into corpus callosum anomalies, white matter signal abnormalities (WMSA), ventriculomegaly, other abnormalities, or no detectable changes. Group differences were assessed using ANOVA and Kruskal–Wallis tests with Tukey post hoc analysis. Logistic regression, principal component analysis (PCA), and linear discriminant analysis (LDA) were applied. Results: WMSA were identified in 23.3% of patients, followed by other anomalies (27.8%), corpus callosum anomalies (8.9%), ventriculomegaly (8.9%), and no abnormalities (31.1%). Total white matter volume was significantly reduced in pathological groups and was the only independent predictor. PCA identified three principal components reflecting shared temporo-parietal covariance, hemispheric asymmetry, and a white matter-related axis. Exploratory LDA demonstrated partial separation among anomaly categories. Conclusions: Children with ASD in this cohort showed heterogeneous but partially structured MRI alterations involving both focal and global volumetric changes. Reduced total white matter volume was the most consistent multivariable association with structural abnormalities. AI-assisted morphometric analysis may support structural phenotyping in ASD. These findings are exploratory and require confirmation in larger, prospectively validated cohorts before biomarker applications can be considered.