A deep learning model for brain segmentation across pediatric and adult populations.

Automated quantification of brain tissues on MR images has greatly contributed to the diagnosis and follow-up of neurological pathologies across various life stages. However, existing solutions are specifically designed for certain age ranges, limiting their applicability in monitoring brain development from infancy to late adulthood. This retrospective study aims to develop and validate a brain segmentation model across pediatric and adult populations. First, we trained a deep learning model to segment tissues and brain structures using T1-weighted MR images from 390 patients (age range: 2-81 years) across four different datasets. Subsequently, the model was validated on a cohort of 280 patients from six distinct test datasets (age range: 4-90 years). In the initial experiment, the proposed deep learning-based pipeline, icobrain-dl, demonstrated segmentation accuracy comparable to both pediatric and adult-specific models across diverse age groups. Subsequently, we evaluated intra- and inter-scanner variability in measurements of various tissues and structures in both pediatric and adult populations computed by icobrain-dl. Results demonstrated significantly higher reproducibility compared to similar brain quantification tools, including childmetrix, FastSurfer, and the medical device icobrain v5.9 (p-value< 0.01). Finally, we explored the potential clinical applications of icobrain-dl measurements in diagnosing pediatric patients with Cerebral Visual Impairment and adult patients with Alzheimer's Disease.

Artificial Intelligence Assistive Software Tool for Automated Detection and Quantification of Amyloid-Related Imaging Abnormalities.

Importance: Amyloid-related imaging abnormalities (ARIA) are brain magnetic resonance imaging (MRI) findings associated with the use of amyloid-ß-directed monoclonal antibody therapies in Alzheimer disease (AD). ARIA monitoring is important to inform treatment dosing decisions and might be improved through assistive software. Objective: To assess the clinical performance of an artificial intelligence (AI)-based software tool for assisting radiological interpretation of brain MRI scans in patients monitored for ARIA. Design, Setting, and Participants: This diagnostic study used a multiple-reader multiple-case design to evaluate the diagnostic performance of radiologists assisted by the software vs unassisted. The study enrolled 16 US Board of Radiology-certified radiologists to perform radiological reading with (assisted) and without the software (unassisted). The study encompassed 199 retrospective cases, where each case consisted of a predosing baseline and a postdosing follow-up MRI of patients from aducanumab clinical trials PRIME, EMERGE, and ENGAGE. Statistical analysis was performed from April to July 2023. Exposures: Use of icobrain aria, an AI-based assistive software for ARIA detection and quantification. Main Outcomes and Measures: Coprimary end points were the difference in diagnostic accuracy between assisted and unassisted detection of ARIA-E (edema and/or sulcal effusion) and ARIA-H (microhemorrhage and/or superficial siderosis) independently, assessed with the area under the receiver operating characteristic curve (AUC). Results: Among the 199 participants included in this study of radiological reading performance, mean (SD) age was 70.4 (7.2) years; 105 (52.8%) were female; 23 (11.6%) were Asian, 1 (0.5%) was Black, 157 (78.9%) were White, and 18 (9.0%) were other or unreported race and ethnicity. Among the 16 radiological readers included, 2 were specialized neuroradiologists (12.5%), 11 were male individuals (68.8%), 7 were individuals working in academic hospitals (43.8%), and they had a mean (SD) of 9.5 (5.1) years of experience. Radiologists assisted by the software were significantly superior in detecting ARIA than unassisted radiologists, with a mean assisted AUC of 0.87 (95% CI, 0.84-0.91) for ARIA-E detection (AUC improvement of 0.05 [95% CI, 0.02-0.08]; P = .001]) and 0.83 (95% CI, 0.78-0.87) for ARIA-H detection (AUC improvement of 0.04 [95% CI, 0.02-0.07]; P = .001). Sensitivity was significantly higher in assisted reading compared with unassisted reading (87% vs 71% for ARIA-E detection; 79% vs 69% for ARIA-H detection), while specificity remained above 80% for the detection of both ARIA types. Conclusions and Relevance: This diagnostic study found that radiological reading performance for ARIA detection and diagnosis was significantly better when using the AI-based assistive software. Hence, the software has the potential to be a clinically important tool to improve safety monitoring and management of patients with AD treated with amyloid-ß-directed monoclonal antibody therapies.

Characterizing Neisseria meningitidis in Southern Vietnam between 2012 and 2021: A predominance of the chloramphenicol-resistant ST-1576 lineage.

Objectives: Our goal was to describe Invasive Meningococcal Disease (IMD) in Southern Vietnam over the last 10 years. We characterized 109 Neisseria meningitidis strains in Southern Vietnam isolated between 1980s to 2021, that were collected from IMD (n = 44), sexually transmitted infections (n = 2), and healthy carriage (n = 63). Methods: IMD were confirmed by bacterial culture and/or real-time polymerase chain reaction at the national reference laboratory in Pasteur Institute of Ho Chi Minh City (PIHCM). Antimicrobial resistance was determined on 31 IMD and two sexually transmitted infection isolates with E-test for chloramphenicol (CHL), penicillin (PEN), ciprofloxacin (CIP), ceftriaxone (CRO), and rifampicin (RIF). Sequencing was performed for analyzing of multilocus-sequence-typing (MLST), porA, fetA, and antibiotic resistance genes, including gyrA, penA, and rpoB. Results: The incidence rate during this period was 0.02 per 100,000 persons/year. Serogroup B accounted for over 90% of cases (50/54). ST-1576 were mainly responsible for IMD, 27/42 MLST profiles, and associated with CHL resistance. Resistance was prevalent among IMD isolates. Thirteen were resistant to CHL (minimum inhibitory concentration [MIC] ≥16 mg/l), 12 were intermediate to PEN (MIC between 0.19 and 0.5 mg/l), and five were CIP-resistant (MIC between 0.19 and 0.5 mg/l). Particularly, one was non-susceptible to CRO (MIC at 0.125 mg/l), belonging to ST-5571 lineage. The resistance was due to carrying resistant alleles of penA and gyrA genes, and catP gene. Notably, seven isolates were resistant/non-susceptible to two or more antibiotics. Conclusion: Our results suggest the persistence of the circulating ST-1576 in Southern Vietnam, with a spread of antimicrobial resistance across the community.