Background – Epidemiology MS
Multiple sclerosis (MS) is the most common inflammatory-demyelinating disease of the central nervous system.
With an prevalence greater than 100 per 100,000 is affects over 2 million people worldwide.
[1,
2]
It is the most frequent cause of non-traumatic neurological disability in young and middle-aged adults [3] and there has been an increasing incidence over the last decades.
[4]
Two third of the patients are women and the typical age of manifestation is between 20 and 40 years.
[1]
The symptoms are characterised by great variety and diversity and in fact there are no neurological symptoms which cannot be related to MS.
[1]
Background – Diagnosis of MS
MS is a clinical diagnosis,
which needs detailed medical history,
clinical examination and supportive paraclinical investigations like MRI,
CSF or evoked potentials.
Symptoms have to be disseminated in time and space and must not be caused by another disease.
[5]
Demyelinating white matter lesions are a marker for disease activity,
severity of symptoms and the progression of disability and hence became a useful parameter for diagnostics and monitoring of MS.
[6,
7]
MRI is the most sensitive technique for detection of MS-plaque [8].
Hence it became important for the detection of changes in lesion load in T2-weighted images (T2ll) in order to monitor both disease activity and effects of therapy.
In clinical routine T2ll is assessed by visual evaluation of the MR images which is a time consuming task and prone to user bias.
Therefore it is desirable to support the assessment of changes in T2ll (cT2ll) with computer-based tools.
Until today plenty of algorithms have been developed for lesion detection and volumetry,
[9,
10,
11,
12,
13,
14,
15] but no gold standard has been defined yet.
Furthermore,
the majority of these techniques have only been evaluated in small trials.
Aim of the Study
The aim of the present study was to evaluate diagnostic yield and accuracy of semi-automatic detection of changes in T2ll (sadcT2ll) changes in comparison to visual analysis.