BACKGROUND AND PURPOSE: Artificial intelligence algorithms have the potential to become an important diagnostic tool to optimize stroke workflow. Viz LVO is a medical product leveraging a convolutional neural network designed to detect large-vessel occlusions on CTA scans and notify the treatment team within minutes via a dedicated mobile application. We aimed to evaluate the detection accuracy of the Viz LVO in real clinical practice at a comprehensive stroke center. MATERIALS AND METHODS: Viz LVO was installed for this study in a comprehensive stroke center. All consecutive head and neck CTAs performed from January 2018 to March 2019 were scanned by the algorithm for detection of large-vessel occlusions. The system results were compared with the formal reports of senior neuroradiologists used as ground truth for the presence of a large-vessel occlusion. RESULTS: A total of 1167 CTAs were included in the study. Of these, 404 were stroke protocols. Seventy-five (6.4%) patients had a large-vessel occlusion as ground truth; 61 were detected by the system. Sensitivity was 0.81, negative predictive value was 0.99, and accuracy was 0.94. In the stroke protocol subgroup, 72 (17.8%) of 404 patients had a large-vessel occlusion, with 59 identified by the system, showing a sensitivity of 0.82, negative predictive value of 0.96, and accuracy of 0.89. CONCLUSIONS: Our experience evaluating Viz LVO shows that the system has the potential for early identification of patients with stroke with large-vessel occlusions, hopefully improving future management and stroke care.

The herpes simplex viruses consist of the strains, HSV-1 and HSV-2, which are prevalent worldwide and lack a definitive cure. We aimed to explore the specific characteristics of HSV 1 and 2 infections, such as differences between gender assigned at birth, age at infection, site of infection, comorbidities, and effect of pregnancy, through a data analysis. Between 2011 and 2018, the Israeli Central Virology Laboratory diagnosed 9189 samples using multiplexed real-time PCR. In addition, we extracted all of the medical data for 287 females hospitalized at the Sheba Medical Center with HSV-1 (161) or HSV-2 (126) genital infections. HSV-2 was almost absent in the orofacial samples from both genders, while in other lesion sites, HSV-2 was significantly more abundant in females than in males (p < 0.05,). HSV-2 was initially detected at puberty. In the hospitalized females’ malignancies, both HSV-1 and HSV-2 were found with a non-significant difference. Simultaneously, pregnancies were more common in females who were HSV-2-positive compared with those who were HSV-1-positive (27.8% vs. 12.4%, respectively, p < 0.01). Primary infections occur more with HSV-1 than with HSV-2 (15.6% vs. 3.2%, respectively). Our findings demonstrate that genital HSV-2 infection episodes are more frequent during pregnancy, suggesting that pregnancy may serve as a risk factor for HSV-2 reactivation or infection.

Pediatric low-grade gliomas (pLGGs) are the most common brain tumors in children and frequently harbor BRAF alterations, most commonly KIAA1549-BRAF fusions and BRAF V600E mutations, which have distinct therapeutic implications. The aim of our study was to assess multiclass radiomics-based prediction of BRAF mutation status in children with pLGG using multi-sequence MRI. This study follows TRIPOD-AI guidelines. This retrospective bi-institutional study included pediatric patients with pLGG and known BRAF mutation status who underwent pre-surgical MRI between January 2009 and January 2023. Tumors were manually segmented, and radiomics features were extracted using PyRadiomics. Random Forest classifiers were trained for three-class classification (BRAF fusion vs BRAF V600E vs non-BRAF) using clinical-only, radiomics-only, and combined models. Performance was evaluated with leave-one-out cross-validation, and results were compared across single-sequence and multisequence approaches. Single-sequence models were trained using all available patients for each MRI sequence, whereas multisequence models were restricted to the subset of 180 patients with all four sequences available. 511 children were included (mean age 8.5 ± 5.1 years; 45% female). Molecular subtypes included BRAF fusion (223/511, 44.6%), BRAF V600E (105/511, 21.0%), and non-BRAF tumors (172/511, 34.4%). FLAIR sequences were available for 495, T2WI for 454, contrast-enhanced T1WI (CE-T1WI) for 285 and ADC maps for 252 children. All sequences were available for 180 children. FLAIR was the best-performing single sequence (AUC 0.82), followed by T2WI (0.80), ADC (0.77), and CE-T1WI (0.75). Reported AUC values represent macro-average one-vs-rest performance across the three molecular classes. Combined clinical-radiomics models consistently outperformed single-source models. In the 180-patient multisequence cohort, radiomics feature concatenation (macro-AUC 0.79) and ensemble modeling (0.79) both outperformed single-sequence approaches (p < 0.001). Feature analysis showed FLAIR-derived features dominated, but adding T2, ADC, and CE-T1WI improved balanced classification across subtypes. MRI-based machine learning models may support noninvasive prediction of BRAF mutation status in pLGG. FLAIR is the best-predicting single sequence, but multisequence integration was associated with improved and more balanced performance. These findings support multisequence radiomics as a promising tool to guide precision treatment in pLGG, particularly when tissue sampling is not feasible.