ArticleSaudi journal of biological sciences2020
The risk prediction of Alzheimer's disease based on the deep learning model of brain 18F-FDG positron emission tomography.
Article in Saudi journal of biological sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 50 citations in OpenAlex.
- Transfer Learning for Alzheimer's Disease through Neuroimaging Biomarkers: A Systematic Review.Sensors (Basel, Switzerland) · 2021Pooled it
- Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer's disease dementia: a systematic review.Alzheimer's research & therapy · 2021Pooled it
- Artificial intelligence techniques for classification of Alzheimer's disease using neuroimaging data: a review.Frontiers in artificial intelligence · 2026Review
- A dynamic fuzzy rule-based inference system using fuzzy inference with semantic reasoning.Scientific reports · 2024Article
- Optimized Convolutional Fusion for Multimodal Neuroimaging in Alzheimer's Disease Diagnosis: Enhancing Data Integration and Feature Extraction.Journal of personalized medicine · 2023Article
- A Comprehensive Review of Artificial Intelligence Based Algorithms Regarding Temporomandibular Joint Related Diseases.Diagnostics (Basel, Switzerland) · 2023Review
- Automated Medical Diagnosis of Alzheimer´s Disease Using an Efficient Net Convolutional Neural Network.Journal of medical systems · 2023Article
- Conventional machine learning and deep learning in Alzheimer's disease diagnosis using neuroimaging: A review.Frontiers in computational neuroscience · 2023Review
- Recent Advances in Cardiovascular Diseases Research Using Animal Models and PET Radioisotope Tracers.International journal of molecular sciences · 2022Review
- Effect of Denoising and DeblurringMetabolites · 2022Article
- BMNet: A New Region-Based Metric Learning Method for Early Alzheimer's Disease Identification With FDG-PET Images.Frontiers in neuroscience · 2022Article
- Automated Synapse Detection Method for Cerebellar Connectomics.Frontiers in neuroanatomy · 2022Article
- Alzheimer's Disease Prediction Algorithm Based on Group Convolution and a Joint Loss Function.Computational and mathematical methods in medicine · 2022Article
- Toward attention-based learning to predict the risk of brain degeneration with multimodal medical data.Frontiers in neuroscience · 2022Article
Corrections and comments
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2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In order to predict the risks of Alzheimer's Disease (AD) based on the deep learning model of brain 18F-FDG positron emission tomography (PET), a total of 350 mild cognitive impairment (MCI) participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were selected as the research objects; in addition, the Convolutional Architecture for Fast Feature Embedding (CAFFE) was selected as the framework of the deep learning platform; the FDG PET image features of each participant were extracted by a deep convolution network model to construct the prediction and classification models; therefore, the MCI stage features were classified and the transformation was predicted. The results showed that in terms of the MCI transformation prediction, the sensitivity and specificity of conv3 classification were respectively 91.02% and 77.63%; in terms of the Late Mild Cognitive Impairment (LMCI) and Early Mild Cognitive Impairment (EMCI) classification, the accuracy of conv5 classification was 72.19%, and the sensitivity and specificity of conv5 were all 73% approximately. Thus, it was seen that the model constructed in the research could be used to solve the problems of MCI transformation prediction, which also had certain effects on the classifications of EMCI and LMCI. The risk prediction of AD based on the deep learning model of brain 18F-FDG PET discussed in the research matched the expected results. It provided a relatively accurate reference model for the prediction of AD. Despite the deficiencies of the research process, the research results have provided certain references and guidance for the future exploration of accurate AD prediction model; therefore, the research is of great significance.
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