Automated volumetric assessment with artificial neural networks might enable a more accurate assessment of disease burden in patients with multiple sclerosis.
Gianluca BrugnaraFabian IsenseeUlf NeubergerDavid BonekampJens PetersenRicarda DiemBrigitte WildemannSabine HeilandWolfgang WickMartin BendszusKlaus Maier-HeinPhillipp VollmuthPublished in: European radiology (2020)
• Artificial neural networks (ANN) can accurately detect and segment both T2/FLAIR and contrast-enhancing MS lesions in MRI data. • Performance of the ANN was consistent in a clinically derived dataset, with patients presenting all possible disease stages in MRI scans acquired from standard clinical routine rather than with high-quality research sequences. • Computer-aided evaluation of MS with ANN could streamline both clinical and research procedures in the volumetric assessment of MS disease burden as well as in lesion detection.
Keyphrases
- neural network
- mass spectrometry
- contrast enhanced
- multiple sclerosis
- ms ms
- magnetic resonance imaging
- end stage renal disease
- computed tomography
- ejection fraction
- machine learning
- newly diagnosed
- magnetic resonance
- risk factors
- prognostic factors
- big data
- electronic health record
- clinical practice
- single cell
- patient reported outcomes
- data analysis
- clinical evaluation