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.

Safe and effective profile of the VSTENT bioresorbable polymer sirolimus-eluting stent in the treatment of patients with de novo coronary artery lesions: a prospective, cohort, multicenter study.

Background: The drug-eluting stent was a significant stride forward in the development of enhanced therapeutic therapy for coronary intervention, with three generations of increased advancement. VSTENT is a newly developed stent manufactured in Vietnam that aims to provide coronary artery patients with a safe, effective, and cost-efficient option. The purpose of this trial was to determine the efficacy and safety of a new bioresorbable polymer sirolimus-eluting stent called VSTENT. Methods: This is a prospective, cohort, multicenter research in 5 centers of Vietnam. A prespecified subgroup received intravascular ultrasound (IVUS) or optical coherence tomography (OCT) imaging. We determined procedure success and complications during index hospitalization. We monitored all participants for a year. Six-month and 12-month rates of major cardiovascular events were reported. All patients had coronary angiography after 6 months to detect late lumen loss (LLL). Prespecified patients also had IVUS or OCT performed. Results: The rate of device success was 100% (95% CI: 98.3-100%; P<0.001). Major cardiovascular events were 4.7% (95% CI: 1.9-9.4%; P<0.001). The LLL over quantitative coronary angiography (QCA) was 0.08±0.19 mm (95% CI: 0.05-0.10; P<0.001) in the in-stent segment and 0.07±0.31 mm (95% CI: 0.03-0.11; P=0.002) in 5 mm within the two ends of the stent segment. The LLL recorded by IVUS and OCT at 6 months was 0.12±0.35 mm (95% CI: 0.01-0.22; P=0.028) and 0.15±0.24 mm (95% CI: 0.02-0.28; P=0.024), respectively. Conclusions: This study's device success rates were perfect. IVUS and OCT findings on LLL were favorable at 6-month follow-up. One-year follow-up showed low in-stent restenosis (ISR) and target lesion revascularization (TLR) rates, reflecting few significant cardiovascular events. VSTENT's safety and efficacy make it a promising percutaneous intervention option in developing nations.

Structural brain dynamics across reading development: A longitudinal MRI study from kindergarten to grade 5.

Primary education is the incubator for learning academic skills that help children to become a literate, communicative, and independent person. Over this learning period, nonlinear and regional changes in the brain occur, but how these changes relate to academic performance, such as reading ability, is still unclear. In the current study, we analyzed longitudinal T1 MRI data of 41 children in order to investigate typical cortical development during the early reading stage (end of kindergarten-end of grade 2) and advanced reading stage (end of grade 2-middle of grade 5), and to detect putative deviant trajectories in children with dyslexia. The structural brain change was quantified with a reliable measure that directly calculates the local morphological differences between brain images of two time points, while considering the global head growth. When applying this measure to investigate typical cortical development, we observed that left temporal and temporoparietal regions belonging to the reading network exhibited an increase during the early reading stage and stabilized during the advanced reading stage. This suggests that the natural plasticity window for reading is within the first years of primary school, hence earlier than the typical period for reading intervention. Concerning neurotrajectories in children with dyslexia compared to typical readers, we observed no differences in gray matter development of the left reading network, but we found different neurotrajectories in right IFG opercularis (during the early reading stage) and in right isthmus cingulate (during the advanced reading stage), which could reflect compensatory neural mechanisms.

Investigating the Added Value of FreeSurfer's Manual Editing Procedure for the Study of the Reading Network in a Pediatric Population.

Insights into brain anatomy are important for the early detection of neurodevelopmental disorders, such as dyslexia. FreeSurfer is one of the most frequently applied automatized software tools to study brain morphology. However, quality control of the outcomes provided by FreeSurfer is often ignored and could lead to wrong statistical inferences. Additional manual editing of the data may be a solution, although not without a cost in time and resources. Past research in adults on comparing the automatized method of FreeSurfer with and without additional manual editing indicated that although editing may lead to significant differences in morphological measures between the methods in some regions, it does not substantially change the sensitivity to detect clinical differences. Given that automated approaches are more likely to fail in pediatric-and inherently more noisy-data, we investigated in the current study whether FreeSurfer can be applied fully automatically or additional manual edits of T1-images are needed in a pediatric sample. Specifically, cortical thickness and surface area measures with and without additional manual edits were compared in six regions of interest (ROIs) of the reading network in 5-to-6-year-old children with and without dyslexia. Results revealed that additional editing leads to statistical differences in the morphological measures, but that these differences are consistent across subjects and that the sensitivity to reveal statistical differences in the morphological measures between children with and without dyslexia is not affected, even though conclusions of marginally significant findings can differ depending on the method used. Thereby, our results indicate that additional manual editing of reading-related regions in FreeSurfer has limited gain for pediatric samples.

Automated MRI volumetry as a diagnostic tool for Alzheimer's disease: Validation of icobrain dm.

Brain volumes computed from magnetic resonance images have potential for assisting with the diagnosis of individual dementia patients, provided that they have low measurement error and high reliability. In this paper we describe and validate icobrain dm, an automatic tool that segments brain structures that are relevant for differential diagnosis of dementia, such as the hippocampi and cerebral lobes. Experiments were conducted in comparison to the widely used FreeSurfer software. The hippocampus segmentations were compared against manual segmentations, with significantly higher Dice coefficients obtained with icobrain dm (25-75th quantiles: 0.86-0.88) than with FreeSurfer (25-75th quantiles: 0.80-0.83). Other brain structures were also compared against manual delineations, with icobrain dm showing lower volumetric errors overall. Test-retest experiments show that the precision of all measurements is higher for icobrain dm than for FreeSurfer except for the parietal cortex volume. Finally, when comparing volumes obtained from Alzheimer's disease patients against age-matched healthy controls, all measures achieved high diagnostic performance levels when discriminating patients from cognitively healthy controls, with the temporal cortex volume measured by icobrain dm reaching the highest diagnostic performance level (area under the receiver operating characteristic curve = 0.99) in this dataset.

Evaluation of methods for volumetric analysis of pediatric brain data: The childmetrix pipeline versus adult-based approaches.

Pediatric brain volumetric analysis based on Magnetic Resonance Imaging (MRI) is of particular interest in order to understand the typical brain development and to characterize neurodevelopmental disorders at an early age. However, it has been shown that the results can be biased due to head motion, inherent to pediatric data, and due to the use of methods based on adult brain data that are not able to accurately model the anatomical disparity of pediatric brains. To overcome these issues, we proposed childmetrix, a tool developed for the analysis of pediatric neuroimaging data that uses an age-specific atlas and a probabilistic model-based approach in order to segment the gray matter (GM) and white matter (WM). The tool was extensively validated on 55 scans of children between 5 and 6 years old (including 13 children with developmental dyslexia) and 10 pairs of test-retest scans of children between 6 and 8 years old and compared with two state-of-the-art methods using an adult atlas, namely icobrain (applying a probabilistic model-based segmentation) and Freesurfer (applying a surface model-based segmentation). The results obtained with childmetrix showed a better reproducibility of GM and WM segmentations and a better robustness to head motion in the estimation of GM volume compared to Freesurfer. Evaluated on two subjects, childmetrix showed good accuracy with 82-84% overlap with manual segmentation for both GM and WM, thereby outperforming the adult-based methods (icobrain and Freesurfer), especially for the subject with poor quality data. We also demonstrated that the adult-based methods needed double the number of subjects to detect significant morphological differences between dyslexics and typical readers. Once further developed and validated, we believe that childmetrix would provide appropriate and reliable measures for the examination of children's brain.

Processing of structural neuroimaging data in young children: Bridging the gap between current practice and state-of-the-art methods.

The structure of the brain is subject to very rapid developmental changes during early childhood. Pediatric studies based on Magnetic Resonance Imaging (MRI) over this age range have recently become more frequent, with the advantage of providing in vivo and non-invasive high-resolution images of the developing brain, toward understanding typical and atypical trajectories. However, it has also been demonstrated that application of currently standard MRI processing methods that have been developed with datasets from adults may not be appropriate for use with pediatric datasets. In this review, we examine the approaches currently used in MRI studies involving young children, including an overview of the rationale for new MRI processing methods that have been designed specifically for pediatric investigations. These methods are mainly related to the use of age-specific or 4D brain atlases, improved methods for quantifying and optimizing image quality, and provision for registration of developmental data obtained with longitudinal designs. The overall goal is to raise awareness of the existence of these methods and the possibilities for implementing them in developmental neuroimaging studies.

Automatic Screening and Grading of Age-Related Macular Degeneration from Texture Analysis of Fundus Images.

Age-related macular degeneration (AMD) is a disease which causes visual deficiency and irreversible blindness to the elderly. In this paper, an automatic classification method for AMD is proposed to perform robust and reproducible assessments in a telemedicine context. First, a study was carried out to highlight the most relevant features for AMD characterization based on texture, color, and visual context in fundus images. A support vector machine and a random forest were used to classify images according to the different AMD stages following the AREDS protocol and to evaluate the features' relevance. Experiments were conducted on a database of 279 fundus images coming from a telemedicine platform. The results demonstrate that local binary patterns in multiresolution are the most relevant for AMD classification, regardless of the classifier used. Depending on the classification task, our method achieves promising performances with areas under the ROC curve between 0.739 and 0.874 for screening and between 0.469 and 0.685 for grading. Moreover, the proposed automatic AMD classification system is robust with respect to image quality.