Exploring New Tools in Upper Limb Rehabilitation After Stroke Using an Exoskeletal Aid: A Pilot Randomized Control Study.

Background/objectives: Spasticity commonly occurs in individuals after experiencing a stroke, impairing their hand function and limiting activities of daily living (ADLs). In this paper, we introduce an exoskeletal aid, combined with a set of augmented reality (AR) games consisting of the Rehabotics rehabilitation solution, designed for individuals with upper limb spasticity following stroke.

Methods: Our study, involving 60 post-stroke patients (mean ± SD age: 70.

Sex Estimation Through Orbital Measurements: A Machine Learning Approach for Forensic Science.

Background: Sex estimation has been extensively investigated due to its importance for forensic science. Several anatomical structures of the human body have been used for this process. The human skull has important landmarks that can serve as reliable sex estimation predictors.

Τhe styloid process length and the stylohyoid chain ossification affect its relationship with the carotid arteries.

Purpose: The elongated styloid process (SP) is associated with internal carotid artery (ICA) dissection, due to its close relationship with the vessel. However, the relationship with the variable ossified stylohyoid chain (SHC) has been scarcely studied. The current study investigated the relationship between the temporal bone's SP and the external and internal carotid arteries (ECA and ICA).

Enhancing Sports Injury Risk Assessment in Soccer Through Machine Learning and Training Load Analysis.

Sports injuries pose significant challenges in athlete welfare and team dynamics, particularly in high-intensity sports like soccer. This study used machine learning algorithms to assess non-contact injury risk in professional male soccer players from physiological and mechanical load variables. Twenty-five professional male soccer players with a first-time, non-contact muscle injury were included in this study.

ProLesA-Net: A multi-channel 3D architecture for prostate MRI lesion segmentation with multi-scale channel and spatial attentions.

Prostate cancer diagnosis and treatment relies on precise MRI lesion segmentation, a challenge notably for small (<15 mm) and intermediate (15-30 mm) lesions. Our study introduces ProLesA-Net, a multi-channel 3D deep-learning architecture with multi-scale squeeze and excitation and attention gate mechanisms. Tested against six models across two datasets, ProLesA-Net significantly outperformed in key metrics: Dice score increased by 2.