Background Early amyloid deposition results in functional and structural brain alterations in predementia stages of Alzheimer's disease (AD). However, how early functional and structural brain changes are related ...
Background Early amyloid deposition results in functional and structural brain alterations in predementia stages of Alzheimer's disease (AD). However, how early functional and structural brain changes are related to each other remains unclear. Investigating the simultaneous disruptions of functional-structural brain features within individuals in relation to amyloid deposition may increase our understanding of the neuropathological processes underlying AD. Method We included 648 non-demented participants from the European Prevention for Alzheimer’s Dementia (EPAD) cohort vIMI baseline data release. The cut-off for cerebrospinal fluid (CSF) amyloid-β positivity (A+) was defined at <1000pg/mL (Elecsys assay). Functional connectivity was estimated with functional eigenvector centrality (EC) from the resting state functional MRI. White matter (WM) integrity was estimated with fractional anisotropy (FA), computed on diffusion MRI. Both measures were computed at the voxel level within the gray and white matter, respectively. First, we used linear regression models to investigate differences between A+ and A- participants within each modality separately, adjusting for age, sex, and site. In the whole group, we performed sparse canonical correlation analysis (sCCA) on functional and structural measures, identifying shared components between the two modalities. Individual sCCA scores were compared between amyloid groups using a generalized linear model (GLM). To evaluate this association in A+ individuals, the same sCCA analysis was performed normalizing the structural and functional matrices using the mean and SD from A- participants. Results Sample characteristics are provided in Table 1 . Differences in voxelwise EC and FA between amyloid groups are shown in Figure 1 . In the whole group, sCCA analysis showed that EC in the default mode, executive and cerebellar networks covary with FA in parietal, temporal and cerebellar areas ( Figure 2 ). sCCA projections were differen
image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on ...
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Adolescents with obesity face numerous health risks and encounter barriers that lead to physical inactivity. We developed a virtual reality sports system, named REVERIE (Real-World Exercise and VR-Based Exercise Resea...
Adolescents with obesity face numerous health risks and encounter barriers that lead to physical inactivity. We developed a virtual reality sports system, named REVERIE (Real-World Exercise and VR-Based Exercise Research in Education), which used deep reinforcement learning to train transformer-based virtual coaching agents, offering immersive and effective sports guidance, with biomechanical performance comparable to real-world physical sports. We integrated REVERIE into a randomized controlled trial involving an 8-week intervention in adolescents with excess body weight (n = 227). Participants were randomized (1:1:1:1:1) to physical table tennis, physical soccer, REVERIE table tennis, REVERIE soccer or control. REVERIE sports intervention was effective in reducing primary outcome fat mass (mean -4.28 kg (95% confidence interval (CI) -6.35 to -2.22), relative to control), with no significant difference compared with physical sports (mean -5.06 kg (95% CI -7.13 to -2.98), relative to control). For secondary outcomes, decreases in liver enzymes and low-density lipoprotein cholesterol levels were found in physical and REVERIE sports groups compared to control. Physical and REVERIE sports showed improvements in physical fitness, psychological well-being and sports willingness after an 8-week intervention, which remained at the 6-month follow-up in the REVERIE sports group. REVERIE sports demonstrated superior cognitive enhancements compared to physical sports in exploratory analyses, as evidenced by olfactory tests (total score: mean 2.84 (95% CI 1.15 to 4.53)) and working memory paradigm (2-back accuracy: mean 10.88% (95% CI 1.19% to 20.56%)). Functional magnetic resonance imaging exhibited that REVERIE sports enhanced neural efficiency and neuroplasticity. Multi-omics analyses revealed distinct changes induced by REVERIE sports that were closely associated with cognitive improvement. Minimal injury rates were 7.69% for REVERIE and 13.48% for physical sports, with no
Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology ...
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Computational competitions are the standard for benchmarking medical image analysis algorithms, but they typically use small curated test datasets acquired at a few centers, leaving a gap to the reality of diverse mul...
Computational competitions are the standard for benchmarking medical image analysis algorithms, but they typically use small curated test datasets acquired at a few centers, leaving a gap to the reality of diverse multicentric patient data. To this end, the Federated Tumor Segmentation (FeTS) Challenge represents the paradigm for real-world algorithmic performance evaluation. The FeTS challenge is a competition to benchmark (i) federated learning aggregation algorithms and (ii) state-of-the-art segmentation algorithms, across multiple international sites. Weight aggregation and client selection techniques were compared using a multicentric brain tumor dataset in realistic federated learning simulations, yielding benefits for adaptive weight aggregation, and efficiency gains through client sampling. Quantitative performance evaluation of state-of-the-art segmentation algorithms on data distributed internationally across 32 institutions yielded good generalization on average, albeit the worst-case performance revealed data-specific modes of failure. Similar multi-site setups can help validate the real-world utility of healthcare AI algorithms in the future.
Previous foundation models for fundus images were pre-trained with limited disease categories and knowledge base. Here we introduce RetiZero, a vision-language model that incorporates knowledge from over 400 fundus di...
Previous foundation models for fundus images were pre-trained with limited disease categories and knowledge base. Here we introduce RetiZero, a vision-language model that incorporates knowledge from over 400 fundus diseases. The model is pre-trained on 341,896 fundus images with accompanying text descriptions gathered from diverse sources across multiple ethnicities and countries. RetiZero demonstrates exceptional performance across various downstream tasks including zero-shot disease recognition, image-to-image retrieval, clinical diagnosis assistance, few-shot fine-tuning, and cross-domain disease identification. In zero-shot scenarios, it achieves Top-5 accuracies of 0.843 for 15 diseases and 0.756 for 52 diseases, while for image-to-image retrieval, it scores 0.950 and 0.886 respectively. Notably, RetiZero's Top-3 zero-shot performance exceeds the average diagnostic accuracy of 19 ophthalmologists from Singapore, China, and the United States. The model particularly enhances clinicians' ability to diagnose rare fundus conditions, highlighting its potential value for integration into clinical settings where diverse eye diseases are encountered.
Background Manual contouring for radiation therapy planning remains the most laborious and time consuming part in the radiation therapy workflow. Particularly for cervical cancer, this task is complicated by the compl...
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Background Manual contouring for radiation therapy planning remains the most laborious and time consuming part in the radiation therapy workflow. Particularly for cervical cancer, this task is complicated by the complex female pelvic anatomy and the concomitant dependence on 18 F-labeled Fluorodeoxyglucose (FDG) positron emission tomography (PET) and magnetic resonance (MR) images. Using deep learning, we propose a new auto-contouring method for FDG-PET/MR based cervical cancer radiation therapy by combining the high level anatomical topography and radiological properties, to the low-level pixel wise deep-learning based semantic segmentation. Materials/methods The proposed method: 1) takes advantage of PET data and left/right anatomical symmetry, creating sub-volumes that are centered on the structures to be contoured. 2) Uses a 3D shallow U-Net (sU-Net) model with an encoder depth of 2.3) Applies the successive training of 3 consecutive sU-Nets in a feed forward strategy. 4) Employs, instead of the usual generalized dice loss function (GDL), a patch dice loss function (PDL) that takes into account the Dice similarity index (DSI) at the level of each training patch. Experimental analysis was conducted on a set of 13 PET/MR images was using a leave-one-out strategy. Results Despite the limited data availability, 5 anatomical structures - the gross tumor volume, bladder, anorectum, and bilateral femurs - were accurately (DSI = 0.78), rapidly (1.9 s/structure), and automatically delineated by our algorithm. Overall, PDL achieved a better performance than GDL and DSI was higher for organs at risk (OARs) with solid tissue (e.g. femurs) than for OARs with air-filled soft tissues (e.g. anorectum). Conclusion The presented workflow successfully addresses the challenge of auto-contouring in FDG-PET/MR based cervical cancer. It is expected to expedite the cervical cancer radiation therapy workflow in both, conventional and adaptive radiation therapy settings.
Many developmental processes, such as plasticity and aging, or pathological processes such as neurological diseases are characterized by modulations of specific cellular types and their microstructures. Diffusion-weig...
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