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@ARTICLE{Volkmann:273979,
      author       = {Volkmann, Heiko and Höglinger, Günter U and Grön, Georg
                      and Barlescu, Lavinia and Müller, Hans-Peter and Kassubek,
                      Jan},
      collaboration = {group, DESCRIBE-PSP study},
      title        = {{MRI} classification of progressive supranuclear palsy,
                      {P}arkinson disease and controls using deep learning and
                      machine learning algorithms for the identification of
                      regions and tracts of interest as potential biomarkers.},
      journal      = {Computers in biology and medicine},
      volume       = {185},
      issn         = {0010-4825},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {DZNE-2024-01428},
      pages        = {109518},
      year         = {2025},
      abstract     = {Quantitative magnetic resonance imaging (MRI) analysis has
                      shown promise in differentiating neurodegenerative
                      Parkinsonian syndromes and has significantly advanced our
                      understanding of diseases like progressive supranuclear
                      palsy (PSP) in recent years.The aim of this study was to
                      develop, implement and compare MRI analysis algorithms based
                      on artificial intelligence (AI) that can differentiate PSP
                      not only from healthy controls but also from Parkinson
                      disease (PD), by analyzing changes in brain structure and
                      microstructure. Specifically, this study focused on
                      identifying regions of interest (ROIs) and tracts of
                      interest (TOIs) that are crucial for the algorithms to
                      provide clinically relevant performance indices for the
                      distinction between disease variants.MR data comprised
                      diffusion tensor imaging (DTI - tractwise fractional
                      anisotropy statistics (TFAS)) and T1-weighted (T1-w) data
                      (texture analysis of the corpus callosum (CC)). One subject
                      sample with 74 PSP patients and 63 controls was recorded at
                      3.0T at multiple sites. The other sample came from a single
                      site, consisting of 66 PSP patients, 66 PD patients, and 44
                      controls, recorded at 1.5T. Four different machine learning
                      algorithms (ML) and a deep learning (DL) neural network
                      approach using Tensor Flow were implemented for the study.
                      The training of the algorithms was performed on 80 $\%$ of
                      the data, which included the entire single-site data and
                      parts of the multiple-site data. The validation process was
                      conducted on the remaining data, thereby consistently
                      separating training and validation data.A random forest
                      algorithm and a DL neural network classified PSP and healthy
                      controls with accuracies of 92 $\%$ and 95 $\%,$
                      respectively. Particularly, DTI derived measures for the
                      pons, midbrain tegmentum, superior cerebral peduncle,
                      putamen, and CC contributed to high accuracies. Furthermore,
                      DL neural network classification of PSP and PD with 86 $\%$
                      accuracy showed the importance of 19 structures. The four
                      most important features were DTI derived measures for
                      prefrontal white matter, the fasciculus frontooccipitalis,
                      the midbrain tegmentum, and the CC area II. This DL network
                      achieved a sensitivity of 88 $\%$ and specificity of 85
                      $\%,$ resulting in a Youden-index of 0.72.The primary goal
                      of the present study was to compare multiple ML-methods and
                      a DL approach to identify the least necessary set of brain
                      structures to classify PSP vs. controls and PSP vs. PD by
                      ranking them in a hierarchical order of importance. That
                      way, this study demonstrated the potential of AI approaches
                      to MRI as possible diagnostic and scientific tools to
                      differentiate variants of neurodegenerative Parkinsonism.},
      keywords     = {Deep learning (Other) / Diffusion tensor imaging (DTI)
                      (Other) / Machine learning (Other) / Magnetic resonance
                      imaging (MRI) (Other) / Neuropathology (Other) / Progressive
                      supranuclear palsy (Other) / tau protein (Other)},
      cin          = {Clinical Research (Munich)},
      ddc          = {570},
      cid          = {I:(DE-2719)1111015},
      pnm          = {353 - Clinical and Health Care Research (POF4-353)},
      pid          = {G:(DE-HGF)POF4-353},
      experiment   = {EXP:(DE-2719)DESCRIBE-PSP-20160101},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:39662313},
      doi          = {10.1016/j.compbiomed.2024.109518},
      url          = {https://pub.dzne.de/record/273979},
}