Data-driven differential diagnosis of dementia using multiclass disease state index classifier

Antti Tolonen, Hanneke F.M. Rhodius-Meester, Marie Bruun, Juha Koikkalainen, Frederik Barkhof, Afina W. Lemstra, Teddy Koene, Philip Scheltens, Charlotte E. Teunissen, Tong Tong, Ricardo Guerrero, Andreas Schuh, Christian Ledig, Marta Baroni, Daniel Rueckert, Hilkka Soininen, Anne M. Remes, Gunhild Waldemar, Steen G. Hasselbalch, Patrizia Mecocci & 2 others Wiesje M. van der Flier, Jyrki Lötjönen

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Clinical decision support systems (CDSSs) hold potential for the differential diagnosis of neurodegenerative diseases. We developed a novel CDSS, 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 CDSSs 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.

Original languageEnglish
Article number111
JournalFrontiers in Aging Neuroscience
Volume10
Issue numberAPR
DOIs
Publication statusPublished - 25 Apr 2018

Cite this

Tolonen, Antti ; Rhodius-Meester, Hanneke F.M. ; Bruun, Marie ; Koikkalainen, Juha ; Barkhof, Frederik ; Lemstra, Afina W. ; Koene, Teddy ; Scheltens, Philip ; Teunissen, Charlotte E. ; Tong, Tong ; Guerrero, Ricardo ; Schuh, Andreas ; Ledig, Christian ; Baroni, Marta ; Rueckert, Daniel ; Soininen, Hilkka ; Remes, Anne M. ; Waldemar, Gunhild ; Hasselbalch, Steen G. ; Mecocci, Patrizia ; van der Flier, Wiesje M. ; Lötjönen, Jyrki. / Data-driven differential diagnosis of dementia using multiclass disease state index classifier. In: Frontiers in Aging Neuroscience. 2018 ; Vol. 10, No. APR.
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title = "Data-driven differential diagnosis of dementia using multiclass disease state index classifier",
abstract = "Clinical decision support systems (CDSSs) hold potential for the differential diagnosis of neurodegenerative diseases. We developed a novel CDSS, 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 CDSSs 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 = "Alzheimer's disease, Classification, Decision support, Dementia with Lewy bodies, Frontotemporal lobar degeneration, Neurodegenerative diseases, Vascular dementia",
author = "Antti Tolonen and Rhodius-Meester, {Hanneke F.M.} and Marie Bruun and Juha Koikkalainen and Frederik Barkhof and Lemstra, {Afina W.} and Teddy Koene and Philip Scheltens and Teunissen, {Charlotte E.} and Tong Tong and Ricardo Guerrero and Andreas Schuh and Christian Ledig and Marta Baroni and Daniel Rueckert and Hilkka Soininen and Remes, {Anne M.} and Gunhild Waldemar and Hasselbalch, {Steen G.} and Patrizia Mecocci and {van der Flier}, {Wiesje M.} and Jyrki L{\"o}tj{\"o}nen",
year = "2018",
month = "4",
day = "25",
doi = "10.3389/fnagi.2018.00111",
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journal = "Frontiers in Aging Neuroscience",
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Tolonen, A, Rhodius-Meester, HFM, Bruun, M, Koikkalainen, J, Barkhof, F, Lemstra, AW, Koene, T, Scheltens, P, Teunissen, CE, Tong, T, Guerrero, R, Schuh, A, Ledig, C, Baroni, M, Rueckert, D, Soininen, H, Remes, AM, Waldemar, G, Hasselbalch, SG, Mecocci, P, van der Flier, WM & Lötjönen, J 2018, 'Data-driven differential diagnosis of dementia using multiclass disease state index classifier' Frontiers in Aging Neuroscience, vol. 10, no. APR, 111. https://doi.org/10.3389/fnagi.2018.00111

Data-driven differential diagnosis of dementia using multiclass disease state index classifier. / Tolonen, Antti; Rhodius-Meester, Hanneke F.M.; Bruun, Marie; Koikkalainen, Juha; Barkhof, Frederik; Lemstra, Afina W.; Koene, Teddy; Scheltens, Philip; Teunissen, Charlotte E.; Tong, Tong; Guerrero, Ricardo; Schuh, Andreas; Ledig, Christian; Baroni, Marta; Rueckert, Daniel; Soininen, Hilkka; Remes, Anne M.; Waldemar, Gunhild; Hasselbalch, Steen G.; Mecocci, Patrizia; van der Flier, Wiesje M.; Lötjönen, Jyrki.

In: Frontiers in Aging Neuroscience, Vol. 10, No. APR, 111, 25.04.2018.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Data-driven differential diagnosis of dementia using multiclass disease state index classifier

AU - Tolonen, Antti

AU - Rhodius-Meester, Hanneke F.M.

AU - Bruun, Marie

AU - Koikkalainen, Juha

AU - Barkhof, Frederik

AU - Lemstra, Afina W.

AU - Koene, Teddy

AU - Scheltens, Philip

AU - Teunissen, Charlotte E.

AU - Tong, Tong

AU - Guerrero, Ricardo

AU - Schuh, Andreas

AU - Ledig, Christian

AU - Baroni, Marta

AU - Rueckert, Daniel

AU - Soininen, Hilkka

AU - Remes, Anne M.

AU - Waldemar, Gunhild

AU - Hasselbalch, Steen G.

AU - Mecocci, Patrizia

AU - van der Flier, Wiesje M.

AU - Lötjönen, Jyrki

PY - 2018/4/25

Y1 - 2018/4/25

N2 - Clinical decision support systems (CDSSs) hold potential for the differential diagnosis of neurodegenerative diseases. We developed a novel CDSS, 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 CDSSs 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.

AB - Clinical decision support systems (CDSSs) hold potential for the differential diagnosis of neurodegenerative diseases. We developed a novel CDSS, 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 CDSSs 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.

KW - Alzheimer's disease

KW - Classification

KW - Decision support

KW - Dementia with Lewy bodies

KW - Frontotemporal lobar degeneration

KW - Neurodegenerative diseases

KW - Vascular dementia

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DO - 10.3389/fnagi.2018.00111

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