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Front. Aging Neurosci. | doi: 10.3389/fnagi.2018.00111

Data-driven differential diagnosis of dementia using multiclass Disease State Index classifier

 Antti Tolonen1*,  Hanneke F. Rhodius-Meester2, Marie Bruun3, Juha Koikkalainen4,  Frederik Barkhof2, 5, Afina W. Lemstra2, Teddy Koene2,  Philip Scheltens2,  Charlotte E. Teunissen2, Tong Tong6, Ricardo Guerrero6, Andreas Schuh6, Christian Ledig6,  Marta Baroni7, Daniel Rueckert6,  Hilkka Soininen8, 9,  Anne M. Remes8, 9, Gunhild Waldemar3, Steen G. Hasselbalch3,  Patrizia Mecocci7, Wiesje M. van der Flier2, 10 and  Jyrki Lötjönen4
  • 1VTT Technical Research Centre of Finland, Finland
  • 2Alzheimer Center, Department of Neurology, Medical Center, VU University Amsterdam, Netherlands
  • 3Danish Dementia Research Centre, Rigshospitalet, Denmark
  • 4Combinostics Oy, Finland
  • 5Institutes of Neurology and Healthcare Engineering, University College London, United Kingdom
  • 6Imperial College London, United Kingdom
  • 7Institute of Gerontology and Geriatrics, University of Perugia, Italy
  • 8Institute of Clinical Medicine, Neurology, University of Eastern Finland, Finland
  • 9Neurocenter, Neurology, University of Eastern Finland, Finland
  • 10Department of Epidemiology and Biostatistics, Medical Center, VU University Amsterdam, Netherlands

Clinical decision support systems hold potential for the differential diagnosis of neurodegenerative diseases. We developed a novel clinical decision support system, the PredictND tool, designed for differential diagnosis of different types of dementia. It combines information obtained from multiple diagnostic tests such as neuropsychological tests, MRI and cerebrospinal fluid samples. Here we evaluated how the classifier used in it performs in differentiating between controls with subjective cognitive decline, dementia due to Alzheimer’s disease, vascular dementia, frontotemporal lobar degeneration and dementia with Lewy bodies.
We used the multiclass Disease State Index classifier, which is the classifier used by the PredictND tool, to differentiate between controls and patients with the four different types of dementia. The multiclass Disease State Index classifier is an extension of a previously developed two-class Disease State Index classifier. As the two-class Disease State Index classifier, the multiclass Disease State Index classifier also offers a visualization of its decision making process, which makes it especially suitable for medical decision support where interpretability of the results is highly important.
A subset of the Amsterdam Dementia cohort, consisting of 504 patients (age 65 ± 8 years, 44 % females) with data from neuropsychological tests, cerebrospinal fluid samples and both automatic and visual MRI quantifications, was used for the evaluation.
The Disease State Index classifier was highly accurate in separating the five classes from each other (balanced accuracy 82.3 %). Accuracy was highest for vascular dementia and lowest for dementia with Lewy bodies. For the 50 % of patients for which the classifier was most confident on the classification the balanced accuracy was 93.6 %.
Data-driven clinical decision support systems can be of aid in differential diagnosis in clinical practice. The decision support system tested in this study was highly accurate in separating the different dementias and controls from each other. In addition to the predicted class, it also provides a confidence measure for the classification.

Keywords: Neurodegenerative Diseases, Classification, Decision Support, Alzheimers disease, Frontotemporal Lobar Degeneration, Vascular Dementia, Dementia with Lewy bodies

Received: 19 Jun 2017; Accepted: 03 Apr 2018.

Edited by:

Catarina Oliveira, University of Coimbra, Portugal

Reviewed by:

Hidenao Fukuyama, Kyoto University, Japan
Keith A. Wesnes, Wesnes Cognition Ltd
Sarat C. Vatsavayai, University of California, San Francisco, United States
Karim Lekadir, Universidad Pompeu Fabra, Spain  

Copyright: © 2018 Tolonen, Rhodius-Meester, Bruun, Koikkalainen, Barkhof, Lemstra, Koene, Scheltens, Teunissen, Tong, Guerrero, Schuh, Ledig, Baroni, Rueckert, Soininen, Remes, Waldemar, Hasselbalch, Mecocci, van der Flier and Lötjönen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mr. Antti Tolonen, VTT Technical Research Centre of Finland, Espoo, Finland,