ArticleBioMed research international2014
Automated identification of dementia using FDG-PET imaging.
Article in BioMed research international, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 22 citations in OpenAlex.
- The Evolving Role of FDG-PET in Behavioral Variant Frontotemporal Dementia: Current Applications and Future Opportunities.International journal of molecular sciences · 2025Review
- The potential role of machine learning and deep learning in differential diagnosis of Alzheimer's disease and FTD using imaging biomarkers: A review.The neuroradiology journal · 2025Review
- Clot burden of acute pulmonary thromboembolism: comparison of two deep learning algorithms, Qanadli score, and Mastora score.Quantitative imaging in medicine and surgery · 2022Article
- Automated differential diagnosis of dementia syndromes using FDG PET and machine learning.Frontiers in aging neuroscience · 2022Article
- BrainFD: Measuring the Intracranial Brain Volume With Fractal Dimension.Frontiers in aging neuroscience · 2021Article
- [Performance of Deep-learning-based Artificial Intelligence on Detection of Pulmonary Nodules in Chest CT].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2019Article
- Selecting the most relevant brain regions to discriminate Alzheimer's disease patients from healthy controls using multiple kernel learning: A comparison across functional and structural imaging modalities and atlases.NeuroImage. Clinical · 2018Article
- Automated identification of dementia using medical imaging: a survey from a pattern classification perspective.Brain informatics · 2016Article
- Pulmonary Nodule Detection Model Based on SVM and CT Image Feature-Level Fusion with Rough Sets.BioMed research international · 2016Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Parametric FDG-PET images offer the potential for automated identification of the different dementia syndromes. However, various existing image features and classifiers have their limitations in characterizing and differentiating the patterns of this disease. We reported a hybrid feature extraction, selection, and classification approach, namely, the GA-MKL algorithm, for separating patients with suspected Alzheimer's disease and frontotemporal dementia from normal controls. In this approach, we extracted three groups of features to describe the average level, spatial variation, and asymmetry of glucose metabolic rates in 116 cortical volumes. An optimal combination of features, that is, capable of classifying dementia cases was identified by a genetic algorithm- (GA-) based method. The condition of each FDG-PET study was predicted by applying the selected features to a multikernel learning (MKL) machine, in which the weighting parameter of each kernel function can be automatically estimated. We compared our approach to two state-of-the-art dementia identification algorithms on a set of 129 clinical cases and improved the performance in separating the dementia types, achieving accuracy of 94.62%. There is a very good agreement between the proposed automated technique and the diagnosis made by clinicians.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.