A multimodal machine learning model for predicting dementia conversion in Alzheimer's disease.
Sci Rep
; 14(1): 12276, 2024 05 29.
Article
en En
| MEDLINE
| ID: mdl-38806509
ABSTRACT
Alzheimer's disease (AD) accounts for 60-70% of the population with dementia. Mild cognitive impairment (MCI) is a diagnostic entity defined as an intermediate stage between subjective cognitive decline and dementia, and about 10-15% of people annually convert to AD. We aimed to investigate the most robust model and modality combination by combining multi-modality image features based on demographic characteristics in six machine learning models. A total of 196 subjects were enrolled from four hospitals and the Alzheimer's Disease Neuroimaging Initiative dataset. During the four-year follow-up period, 47 (24%) patients progressed from MCI to AD. Volumes of the regions of interest, white matter hyperintensity, and regional Standardized Uptake Value Ratio (SUVR) were analyzed using T1, T2-weighted-Fluid-Attenuated Inversion Recovery (T2-FLAIR) MRIs, and amyloid PET (αPET), along with automatically provided hippocampal occupancy scores (HOC) and Fazekas scales. As a result of testing the robustness of the model, the GBM model was the most stable, and in modality combination, model performance was further improved in the absence of T2-FLAIR image features. Our study predicts the probability of AD conversion in MCI patients, which is expected to be useful information for clinician's early diagnosis and treatment plan design.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Imagen por Resonancia Magnética
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Progresión de la Enfermedad
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Tomografía de Emisión de Positrones
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Enfermedad de Alzheimer
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Disfunción Cognitiva
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Aprendizaje Automático
Límite:
Aged
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Aged80
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Female
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Humans
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Male
Idioma:
En
Revista:
Sci Rep
Año:
2024
Tipo del documento:
Article
Pais de publicación:
Reino Unido