A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases
Autoimmune diseases are chronic, multifactorial conditions. Through machine learning (ML),
a branch of the wider field of artificial intelligence, it is possible to extract patterns within …
a branch of the wider field of artificial intelligence, it is possible to extract patterns within …
Machine learning studies on major brain diseases: 5-year trends of 2014–2018
K Sakai, K Yamada - Japanese journal of radiology, 2019 - Springer
Abstract In the recent 5 years (2014–2018), there has been growing interest in the use of
machine learning (ML) techniques to explore image diagnosis and prognosis of therapeutic …
machine learning (ML) techniques to explore image diagnosis and prognosis of therapeutic …
Advances in brain imaging in multiple sclerosis
Brain imaging is increasingly used to support clinicians in diagnosing multiple sclerosis
(MS) and monitoring its progression. However, the role of magnetic resonance imaging …
(MS) and monitoring its progression. However, the role of magnetic resonance imaging …
[HTML][HTML] Automated segmentation of changes in FLAIR-hyperintense white matter lesions in multiple sclerosis on serial magnetic resonance imaging
P Schmidt, V Pongratz, P Küster, D Meier, J Wuerfel… - NeuroImage: Clinical, 2019 - Elsevier
Longitudinal analysis of white matter lesion changes on serial MRI has become an important
parameter to study diseases with white-matter lesions. Here, we build on earlier work on …
parameter to study diseases with white-matter lesions. Here, we build on earlier work on …
[HTML][HTML] A fully convolutional neural network for new T2-w lesion detection in multiple sclerosis
Introduction: Longitudinal magnetic resonance imaging (MRI) has an important role in
multiple sclerosis (MS) diagnosis and follow-up. Specifically, the presence of new T2-w …
multiple sclerosis (MS) diagnosis and follow-up. Specifically, the presence of new T2-w …
Multiple sclerosis lesion activity segmentation with attention-guided two-path CNNs
Multiple sclerosis is an inflammatory autoimmune demyelinating disease that is
characterized by lesions in the central nervous system. Typically, magnetic resonance …
characterized by lesions in the central nervous system. Typically, magnetic resonance …
Recent advances in the longitudinal segmentation of multiple sclerosis lesions on magnetic resonance imaging: a review
M Diaz-Hurtado, E Martínez-Heras, E Solana… - Neuroradiology, 2022 - Springer
Multiple sclerosis (MS) is a chronic autoimmune disease characterized by demyelinating
lesions that are often visible on magnetic resonance imaging (MRI). Segmentation of these …
lesions that are often visible on magnetic resonance imaging (MRI). Segmentation of these …
[HTML][HTML] Automatic detection of lesion load change in Multiple Sclerosis using convolutional neural networks with segmentation confidence
The detection of new or enlarged white-matter lesions is a vital task in the monitoring of
patients undergoing disease-modifying treatment for multiple sclerosis. However, the …
patients undergoing disease-modifying treatment for multiple sclerosis. However, the …
Accuracy of unenhanced MRI in the detection of new brain lesions in multiple sclerosis
P Eichinger, S Schön, V Pongratz, H Wiestler, H Zhang… - Radiology, 2019 - pubs.rsna.org
Background Administration of a gadolinium-based contrast material is widely considered
obligatory for follow-up imaging of patients with multiple sclerosis (MS). However, advances …
obligatory for follow-up imaging of patients with multiple sclerosis (MS). However, advances …
Machine learning approaches in study of multiple sclerosis disease through magnetic resonance images
Multiple sclerosis (MS) is one of the most common autoimmune diseases which is commonly
diagnosed and monitored using magnetic resonance imaging (MRI) with a combination of …
diagnosed and monitored using magnetic resonance imaging (MRI) with a combination of …