delay its evolution and prevent irreversible sequelae and disability progression. Currently, Magnetic Resonance Imaging (MRI) represents an essential nonclinical tool for the detection of a hallmark of the disease, i.e. the presence of demyelinating lesions within cerebral white matter (WM), and, consequently, for the diagnosis of MS early within its course. However, errors in estimating lesions can contribute to a wrong diagnosis, if only the WM lesion load is taken into account, with a more relevant impact in individuals with a reduced lesion load at an initial clinical event, delaying the start of a treatment until a second clinical relapse or after confirming, successively, dissemination
delay its
evolution and prevent irreversible sequelae and disability progression.
Currently, Magnetic Resonance Imaging (MRI) represents an essential
nonclinical tool for the detection of a hallmark of the disease, i.e.
the presence of demyelinating lesions within cerebral white matter (WM),
and, consequently, for the diagnosis of MS early within its course.
However, errors in estimating lesions can contribute to a wrong
diagnosis, if only the WM lesion load is taken into account, with a more
relevant impact in individuals with a reduced lesion load at an initial
clinical event, delaying the start of a treatment until a second clinical relapse or after confirming, successively, dissemination
In this context, this work proposes
an innovative system, employing a multivariate analysis approach, with
the aim of mining and integrating multiple sensitive neuroimaging
markers, including but not limited to the WM lesion load, into
classification models for supporting a more robust diagnosis of
Relapsing-Remitting-MS (RR-MS) already at an initial clinical event. To
this aim, a retrospective study of 81 patients with diagnosis of RR-MS
(39 males and 42 females, 37.3 ± 8.1 years old, age range 20–58) and 29
healthy people of comparable age and gender (15 males and 14 females,
39.7 ± 11.1 years old, age range 22–57) is used. All the individuals
were enrolled at Multiple Sclerosis Centre of the “Federico II”
University Hospital (Naples, Italy). A machine learning method based on
both statistics and Fuzzy Logic, already validated for its desirable
characteristics, is applied to volumetric
▼ Relaxometry based MRI limb segmentation |
▼ Software development of digital MRI brain phantom |
▼ Relaxometry based MRI brain segmentation |
▼ Protein-based biotherapeutics |