التفاصيل البيبلوغرافية
| العنوان: |
A multimodal machine learning model for predicting dementia conversion in Alzheimer’s disease. |
| المؤلفون: |
Lee, Min-Woo, Kim, Hye Weon, Choe, Yeong Sim, Yang, Hyeon Sik, Lee, Jiyeon, Lee, Hyunji, Yong, Jung Hyeon, Kim, Donghyeon, Lee, Minho, Kang, Dong Woo, Jeon, So Yeon, Son, Sang Joon, Lee, Young-Min, Kim, Hyug-Gi, Kim, Regina E. Y., Lim, Hyun Kook |
| المصدر: |
Scientific Reports; 5/28/2024, Vol. 14 Issue 1, p1-10, 10p |
| مستخلص: |
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. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
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