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Cited by 179Dementia and Cognitive Impairment ResearchMedical Image Segmentation TechniquesArtificial Intelligence in Healthcare

Differential diagnosis of mild cognitive impairment and Alzheimer's disease using structural MRI cortical thickness, hippocampal shape, hippocampal texture, and volumetry

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Sørensen, Lauge, Igel, Christian, Pai, Akshay, Balas, Ioana, Anker, Cecilie, Lillholm, Martin, Nielsen, Mads·2017·NeuroImage: Clinical
DOI 10.1016/j.nicl.2016.11.025PMC PMC5237821
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Abstract

This paper presents a brain T1-weighted structural magnetic resonance imaging (MRI) biomarker that combines several individual MRI biomarkers (cortical thickness measurements, volumetric measurements, hippocampal shape, and hippocampal texture). The method was developed, trained, and evaluated using two publicly available reference datasets: a standardized dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the imaging arm of the Australian Imaging Biomarkers and Lifestyle flagship study of ageing (AIBL). In addition, the method was evaluated by participation in the Computer-Aided Diagnosis of Dementia (CADDementia) challenge. Cross-validation using ADNI and AIBL data resulted in a multi-class classification accuracy of 62.7% for the discrimination of healthy normal controls (NC), subjects with mild cognitive impairment (MCI), and patients with Alzheimer's disease (AD). This performance generalized to the CADDementia challenge where the method, trained using the ADNI and AIBL data, achieved a classification accuracy 63.0%. The obtained classification accuracy resulted in a first place in the challenge, and the method was significantly better (McNemar's test) than the bottom 24 methods out of the total of 29 methods contributed by 15 different teams in the challenge. The method was further investigated with learning curve and feature selection experiments using ADNI and AIBL data. The learning curve experiments suggested that neither more training data nor a more complex classifier would have improved the obtained results. The feature selection experiment showed that both common and uncommon individual MRI biomarkers contributed to the performance; hippocampal volume, ventricular volume, hippocampal texture, and parietal lobe thickness were the most important. This study highlights the need for both subtle, localized measurements and global measurements in order to discriminate NC, MCI, and AD simultaneously based on a single structural MRI scan. It is likely that additional non-structural MRI features are needed to further improve the obtained performance, especially to improve the discrimination between NC and MCI.

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  • Using machine learning to quantify neurodegeneration patterns in structural MRI, studies have predicted MCI conversion to AD with modest accuracy ranging from 63 to 85% depending on the cohort, imaging modality and models used.

    “Using machine learning to quantify neurodegeneration patterns in structural MRI, studies have managed to predict MCI conversion to AD with modest accuracy ranging from 63 to 85% depending on the cohort, imaging modality and models used [25–27].”

    From The use of neuroimaging techniques in the early and differential diagnosis of dementia (2023) · cites this paper as [25]

    The Introduction describes a structural-MRI classifier, and the Abstract reports 62.7% and 63.0% accuracy. Those figures concern simultaneous classification of healthy controls, people with MCI, and patients with AD—not prediction that MCI will convert to AD. Our copy does not substantiate the claimed upper figure of 85% for conversion prediction (searched: conversion, 85%). (LLM, conf=0.89)

    Experimental — accuracy not yet measured

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Cited by

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Authors
Sørensen, L., Igel, C., Pai, A., Balas, I., Anker, C., Lillholm, M., & Nielsen, M.
Affiliations
  • University of Copenhagen (DK)
  • Danmarks Radio (DK)
Corresponding author
Sørensen, Lauge
ORCID
Venue
NeuroImage: Clinical
Publisher
Elsevier BV
Published
2017
Volume
13
Pages
470-482
ISSN
2213-1582 (ISSN-L)
DOI
10.1016/j.nicl.2016.11.025
PMC
PMC5237821
OpenAlex
W2559903050
Cited by
179
References
8
Citations by year
2026: 3 · 2025: 18 · 2024: 17 · 2023: 17 · 2022: 30 · 2021: 21 · 2020: 23 · 2019: 25 · 2018: 18 · 2017: 7
FWCI
14.81
Type
journal article
Language
en
Open access
Licence
Locations
  • NeuroImage Clinical (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by-nc-nd)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Danish National Advanced Technology Foundation (034-2011-5)
  • Eurostars (8234)
  • http://caddementia.grand-challenge.org
Topics
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
mild cognitive impairment, Alzheimer's disease, differential diagnosis, structural MRI biomarkers, cortical thickness, hippocampal shape, hippocampal volumetry, ventricular volume, dementia classification, feature selection, learning curve
MeSH terms
Machine Learning, Aged, Aged, 80 and over, Alzheimer Disease, Cerebral Cortex, Diagnosis, Differential, Female, Hippocampus, Humans, Magnetic Resonance Imaging, Male, Biomarkers, Cognitive Dysfunction

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