Automated segmentation of multiple sclerosis (MS) lesions in magnetic resonance imaging (MRI) has become central to both clinical research and routine care. Precise delineation of lesion load and ...

FLAMeS, a new convolutional neural network, enhances MS lesion segmentation accuracy using only T2-weighted FLAIR images, making it more applicable in clinical settings. The algorithm outperformed ...

A recent Npj Digital Medicine study assesses the accuracy and effectiveness of artificial intelligence (AI)-based imaging techniques to diagnose multiple sclerosis (MS). Study: A real-world clinical ...

Multiple sclerosis (MS) is the result of the immune system mistakenly attacking the brain and the spinal cord, causing tissue damage (lesions) throughout. While the underlying mechanism of lesion ...

In recent years, researchers studying multiple sclerosis have concluded that white matter lesion volume in the brain, long considered the best way to monitor MS disease progression, may not be the ...

Changes in NAWM and NAGM are crucial in MS progression, challenging the traditional lesion-centric model. Subtle alterations in myelin integrity, immune cell function, and neuronal connectivity ...

Using advanced methodology, scientists have been able to reveal at the cellular level how lesions in multiple sclerosis develop. Using advanced methodology, scientists in Sweden were able to reveal at ...

Please provide your email address to receive an email when new articles are posted on . Ocrelizumab reduced the number and size of MS-related cortical lesions. Data synthesis featuring a mix of ...

"[These results] show that white matter lesions across all MS patients tend to injure the white matter depression network, which may create a vulnerability for depression in the MS population as a ...

White matter hyperintensities (WMHs) on fluid-attenuated inversion recovery (FLAIR) images are imaging features in various neurological diseases and essential markers for clinical impairment and ...