ArticleJournal of Alzheimer's disease : JAD2021
Blinded Clinical Evaluation for Dementia of Alzheimer's Type Classification Using FDG-PET: A Comparison Between Feature-Engineered and Non-Feature-Engineered Machine Learning Methods.
Article in Journal of Alzheimer's disease : JAD, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.
- Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions.Journal of medical systems · 2023Pooled it
- Brain metabolic imaging-based model identifies cognitive stability in prodromal Alzheimer's disease.Scientific reports · 2025Article
- Review
- Deep learning combining FDG-PET and neurocognitive data accurately predicts MCI conversion to Alzheimer's dementia 3-year post MCI diagnosis.Neurobiology of disease · 2023Article
- Predicting time-to-conversion for dementia of Alzheimer's type using multi-modal deep survival analysis.Neurobiology of aging · 2023Article
- Searching for optimal machine learning model to classify mild cognitive impairment (MCI) subtypes using multimodal MRI data.Scientific reports · 2022Article
- Application of artificial intelligence in brain molecular imaging.Annals of nuclear medicine · 2022Review
- Machine Learning Based Multimodal Neuroimaging Genomics Dementia Score for Predicting Future Conversion to Alzheimer's Disease.Journal of Alzheimer's disease : JAD · 2022Article
- 3D hemisphere-based convolutional neural network for whole-brain MRI segmentation.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2022Article
- Early diagnosis of Alzheimer's disease on ADNI data using novel longitudinal score based on functional principal component analysis.Journal of medical imaging (Bellingham, Wash.) · 2021Article
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Authors and funding
9 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundAdvanced machine learning methods can aid in the identification of dementia risk using neuroimaging-derived features including FDG-PET. However, to enable the translation of these methods and test their usefulness in clinical practice, it is crucial to conduct independent validation on real clinical samples, which has yet to be properly delineated in the current literature.
objectiveIn this paper, we present our efforts to enable such clinical translational through the evaluation and comparison of two machine-learning methods for discrimination between dementia of Alzheimer's type (DAT) and Non-DAT controls.
methodsFDG-PET-based dementia scores were generated on an independent clinical sample whose clinical diagnosis was blinded to the algorithm designers. A feature-engineered approach (multi-kernel probability classifier) and a non-feature-engineered approach (3D convolutional neural network) were analyzed. Both classifiers were pre-trained on cognitively normal subjects as well as subjects with DAT. These two methods provided a probabilistic dementia score for this previously unseen clinical data. Performance of the algorithms were compared against ground-truth dementia rating assessed by experienced nuclear physicians.
resultsBlinded clinical evaluation on both classifiers showed good separation between the cognitively normal subjects and the patients diagnosed with DAT. The non-feature-engineered dementia score showed higher sensitivity among subjects whose diagnosis was in agreement between the machine-learning models, while the feature-engineered approach showed higher specificity in non-consensus cases.
conclusionIn this study, we demonstrated blinded evaluation using data from an independent clinical sample for assessing the performance in DAT classification models in a clinical setting. Our results showed good generalizability for two machine-learning approaches, marking an important step for the translation of pre-trained machine-learning models into clinical practice.
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