Dataset Information

Automated Scoring of Alzheimer's Disease Atrophy Scale with Subtype Classification Using Deep Learning-Based T1-Weighted Magnetic Resonance Image Segmentation.

ABSTRACT:

Background

Application of visual scoring scales for regional atrophy in Alzheimer's disease (AD) in clinical settings is limited by their high time cost and low intra/inter-rater agreement.

Objective

To provide automated atrophy scoring using objective volume driven from deep-learning segmentation methods for AD subtype classification using magnetic resonance imaging (MRI).

Methods

We enrolled 3,959 participants (1,732 cognitively normal [CN], 1594 with mild cognitive impairment [MCI], and 633 with AD). The occupancy indices for each regional volume were calculated by dividing each volume by the size of the lateral and inferior ventricular volumes. MR images from 355 participants (119 CN, 119 MCI, and 117 AD) from three different centers were used for validation. Two neuroradiologists performed visual assessments of the medial temporal, posterior, and global cortical atrophy scores in the frontal lobe using T1-weighted MR images. Images were also analyzed using the deep learning-based segmentation software, Neurophet AQUA. Cutoff values for the three scores were determined using the data distribution according to age. The scoring results were compared for consistency and reliability.

Results

Four volumetric-driven scoring results showed a high correlation with the visual scoring results for AD, MCI, and CN. The overall agreement with human raters was weak-to-moderate for atrophy scoring in CN participants, and good-to-almost perfect in AD and MCI participants. AD subtyping by automated scores also showed usefulness as a research tool.

Conclusions

Determining AD subtypes using automated atrophy scoring for late-MCI and AD could be useful in clinical settings or multicenter studies with large datasets.

SUBMITTER: Choe YS

PROVIDER: S-EPMC11191633 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature

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Publications

Automated Scoring of Alzheimer's Disease Atrophy Scale with Subtype Classification Using Deep Learning-Based T1-Weighted Magnetic Resonance Image Segmentation.

Choe Yeong Sim YS Kim Regina E Y REY Kim Hye Weon HW Kim JeeYoung J Lee Hyunji H Lee Min Kyoung MK Lee Minho M Kim Keun You KY Kim Se-Hong SH Kim Ji-Hoon JH Lee Jun-Young JY Kim Eosu E Kim Donghyeon D Lim Hyun Kook HK

Journal of Alzheimer's disease reports 20240517 1

<h4>Background</h4>Application of visual scoring scales for regional atrophy in Alzheimer's disease (AD) in clinical settings is limited by their high time cost and low intra/inter-rater agreement.<h4>Objective</h4>To provide automated atrophy scoring using objective volume driven from deep-learning segmentation methods for AD subtype classification using magnetic resonance imaging (MRI).<h4>Methods</h4>We enrolled 3,959 participants (1,732 cognitively normal [CN], 1594 with mild cognitive impai ...[more]

PMID: 38910943

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Dataset Information

Automated Scoring of Alzheimer's Disease Atrophy Scale with Subtype Classification Using Deep Learning-Based T1-Weighted Magnetic Resonance Image Segmentation.

Background

Objective

Methods

Results

Conclusions

Publications

Automated Scoring of Alzheimer's Disease Atrophy Scale with Subtype Classification Using Deep Learning-Based T1-Weighted Magnetic Resonance Image Segmentation.

Similar Datasets

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