Efficient segmentation of active and inactive plaques in FLAIR-images using DeepLabV3Plus SE with efficientnetb0 backbone in multiple sclerosis.

This research paper introduces an efficient approach for the segmentation of active and inactive plaques within Fluid-attenuated inversion recovery (FLAIR) images, employing a convolutional neural network (CNN) model known as DeepLabV3Plus SE with the EfficientNetB0 backbone in Multiple sclerosis (MS), and demonstrates its superior performance compared to other CNN architectures. The study encompasses various critical components, including dataset pre-processing techniques, the utilization of the Squeeze and Excitation Network (SE-Block), and the atrous spatial separable pyramid Block to enhance segmentation capabilities. Detailed descriptions of pre-processing procedures, such as removing the cranial bone segment, image resizing, and normalization, are provided. This study analyzed a cross-sectional cohort of 100 MS patients with active brain plaques, examining 5000 MRI slices. After filtering, 1500 slices were utilized for labeling and deep learning. The training process adopts the dice coefficient as the loss function and utilizes Adam optimization. The study evaluated the model's performance using multiple metrics, including intersection over union (IOU), Dice Score, Precision, Recall, and F1-Score, and offers a comparative analysis with other CNN architectures. Results demonstrate the superior segmentation ability of the proposed model, as evidenced by an IOU of 69.87, Dice Score of 76.24, Precision of 88.89, Recall of 73.52, and F1-Score of 80.47 for the DeepLabV3+SE_EfficientNetB0 model. This research contributes to the advancement of plaque segmentation in FLAIR images and offers a compelling approach with substantial potential for medical image analysis and diagnosis.

RehaCom rehabilitation training improves a wide-range of cognitive functions in multiple sclerosis patients.

Multiple sclerosis (MS) is a chronic neurodegenerative disease that impairs cognitive performance. Attention, response control, working memory, and processing speed are highly impaired in MS. On the other hand, RehaCom is a computerized software that improves cognitive dysfunctions. In this study, we aimed to investigate the effect of RehaCom on attention, response control, processing speed, working memory, visuospatial skills, and verbal/non-verbal executive functions in MS patients. Sixty patients were selected randomly and divided into control (n = 30) and experimental (n = 30) groups. Integrated Auditory Visual-2 (IVA-2), Paced Auditory Serial Addition Test (PASAT), Symbol Digit Modalities Test (SDMT), Judgment of Line Orientation (JLO) and The Delis-Kaplan Executive Function System (DKEFS) were used to assess cognitive functions. Patients in the experimental group were treated by RehaCom for 5 weeks (two 60-min sessions per week). Cognitive performance of all patients in both groups was assessed at weeks 5 and 10 (post-test and follow-up stages, respectively). The results showed that RehaCom treatment improved all studied cognitive functions at the post-test stage. This effect also remained at the follow-up stage for some cognitive functions. In conclusion, treatment with RehaCom may have significant therapeutic effects on cognitive dysfunctions in MS patients.

The therapeutic effect of treatment with RehaCom software on verbal performance in patients with multiple sclerosis.

Multiple sclerosis (MS) is characterized by central nervous system lesions that lead to neurological dysfunctions including fatigue, depression and anxiety. MS is affecting almost 2.3 million people around the world, with the significant highest prevalence in the North America. MS also affects different cognitive abilities, such as attention, memory and executive functions. Furthermore, a significant impairment in verbal fluency and naming abilities in patients with MS has been reported. RehaCom, is a software that has improvement effects on cognitive functions. The goal of this research is to investigate the effect of treatment with RehaCom on verbal performance in patients with MS. To select the participants, 60 patients with MS who referred to our clinic were chosen randomly and divided into Control (n = 30) and Experimental (n = 30) groups. The participants in the experimental group were treated by RehaCom software for 10 sessions during 5 weeks (2 sessions per week and each session was 1 h). Controlled Oral Word Association Test (COWAT) and California Verbal Learning Test - Second Edition (CVLT-II), were used to assess verbal performance (verbal fluency, and verbal learning and memory) at weeks 0 (baseline), 5 (post-test) and 10 (follow-up). The results showed that, treatment with RehaCom improved verbal performance in patient with MS, at both post-test and follow-up stages. In conclusion, treatment with RehaCom cognitive rehabilitation software can improve verbal fluency, and verbal learning and memory in patient with MS, possibly by affecting the brain regions involved in language performance.