首页|University of Freiburg Medical Center Reports Findings in Robotics and Machine Learning (Complemental Value of Microstructural and Macrostructural MRI in the Discrimination of Neurodegenerative Parkinson Syndromes)
University of Freiburg Medical Center Reports Findings in Robotics and Machine Learning (Complemental Value of Microstructural and Macrostructural MRI in the Discrimination of Neurodegenerative Parkinson Syndromes)
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NETL
NSTL
Springer Nature
New research on Robotics and Machine Learning is the subject of a report. According to news originating from Freiburg, Germany, by NewsRx correspondents, research stated, “Various MRI-based techniques were tested for the differentiation of neurodegenerative Parkinson syndromes (NPS); the value of these techniques in direct comparison and combination is uncertain. We thus compared the diagnostic performance of macrostructural, single compartmental, and multicompartmental MRI in the differentiation of NPS.” Financial support for this research came from Universitatsklinikum Freiburg. Our news journalists obtained a quote from the research from the University of Freiburg Medical Center, “We retrospectively included patients with NPS, including 136 Parkinson’s disease (PD), 41 multiple system atrophy (MSA) and 32 progressive supranuclear palsy (PSP) and 27 healthy controls (HC). Macrostructural tissue probability values (TPV) were obtained by CAT12. The microstructure was assessed using a mesoscopic approach by diffusion tensor imaging (DTI), neurite orientation dispersion and density imaging (NODDI), and diffusion microstructure imaging (DMI). After an atlas-based read-out, a linear support vector machine (SVM) was trained on a training set (n = 196) and validated in an independent test cohort (n = 40). The diagnostic performance of the SVM was compared for different inputs individually and in combination. Regarding the inputs separately, we observed the best diagnostic performance for DMI. Overall, the combination of DMI and TPV performed best and correctly classified 88% of the patients. The corresponding area under the receiver operating characteristic curve was 0.87 for HC, 0.97 for PD, 1.0 for MSA, and 0.99 for PSP.”
FreiburgGermanyEuropeDiagnostics and ScreeningHealth and MedicineRobotics and Machine Learning
NETL
NSTL
Springer Nature
