Results 71 to 80 of about 546,017 (306)

Prediction of Myasthenia Gravis Worsening: A Machine Learning Algorithm Using Wearables and Patient‐Reported Measures

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background Myasthenia gravis (MG) is a rare disorder characterized by fluctuating muscle weakness with potential life‐threatening crises. Timely interventions may be delayed by limited access to care and fragmented documentation. Our objective was to develop predictive algorithms for MG deterioration using multimodal telemedicine data ...
Maike Stein   +7 more
wiley   +1 more source

Semi-Supervised Self-Taught Deep Learning for Finger Bones Segmentation

open access: yes, 2019
Segmentation stands at the forefront of many high-level vision tasks. In this study, we focus on segmenting finger bones within a newly introduced semi-supervised self-taught deep learning framework which consists of a student network and a stand-alone ...
Chen, Cen   +8 more
core   +1 more source

Cognitive Status in People With Epilepsy in the Republic of Guinea: A Prospective, Case–Control Study

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective People with epilepsy (PWE) may experience cognitive deficits but fail to undergo formal evaluation. This study compares cognitive status between PWE and healthy controls in the West African Republic of Guinea. Methods A cross‐sectional, case–control study was conducted in sequential recruitment phases (July 2024–July 2025) at Ignace ...
Maya L. Mastick   +14 more
wiley   +1 more source

Clinically Relevant Outcome Measures in Women With Adrenoleukodystrophy

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Adrenoleukodystrophy is a rare inherited peroxisomal disease caused by pathogenic variants in the ABCD1 gene located on the X chromosome. Although the most severe central nervous system and adrenal complications typically affect only men with adrenoleukodystrophy, the majority of women develop myeloneuropathy symptoms in adulthood.
Chenwei Yan   +3 more
wiley   +1 more source

Remote Assessment of Ataxia Severity in SCA3 Across Multiple Centers and Time Points

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Spinocerebellar ataxia type 3 (SCA3) is a genetically defined ataxia. The Scale for Assessment and Rating of Ataxia (SARA) is a clinician‐reported outcome that measures ataxia severity at a single time point. In its standard application, SARA fails to capture short‐term fluctuations, limiting its sensitivity in trials.
Marcus Grobe‐Einsler   +20 more
wiley   +1 more source

A survey of the impact of self-supervised pretraining for diagnostic tasks in medical X-ray, CT, MRI, and ultrasound

open access: yesBMC Medical Imaging
Self-supervised pretraining has been observed to be effective at improving feature representations for transfer learning, leveraging large amounts of unlabelled data.
Blake VanBerlo   +2 more
doaj   +1 more source

Cognitive Behavioral Therapy for Youth with Childhood‐Onset Lupus: A Randomized Clinical Trial

open access: yesArthritis Care &Research, Accepted Article.
Objective Our objective was to determine the feasibility and acceptability of the Treatment and Education Approach for Childhood‐onset Lupus (TEACH), a six‐session cognitive behavioral intervention addressing depressive, fatigue, and pain symptoms, delivered remotely to individual youth with lupus by a trained interventionist.
Natoshia R. Cunningham   +29 more
wiley   +1 more source

Weighted Ensemble Self-Supervised Learning

open access: yes, 2022
Ensembling has proven to be a powerful technique for boosting model performance, uncertainty estimation, and robustness in supervised learning. Advances in self-supervised learning (SSL) enable leveraging large unlabeled corpora for state-of-the-art few-shot and supervised learning performance. In this paper, we explore how ensemble methods can improve
Ruan, Yangjun   +6 more
openaire   +2 more sources

Patterning the Void: Combining L‐Systems with Archimedean Tessellations as a Perspective for Tissue Engineering Scaffolds

open access: yesAdvanced Functional Materials, EarlyView.
This study introduces a novel multi‐scale scaffold design using L‐fractals arranged in Archimedean tessellations for tissue regeneration. Despite similar porosity, tiles display vastly different tensile responses (1–100 MPa) and deformation modes. In vitro experiments with hMSCs show geometry‐dependent growth and activity. Over 55 000 tile combinations
Maria Kalogeropoulou   +4 more
wiley   +1 more source

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