ArticleDiagnostics (Basel, Switzerland)2022
Differential Diagnosis of Alzheimer Disease vs. Mild Cognitive Impairment Based on Left Temporal Lateral Lobe Hypomethabolism on
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.
- Neurological autoimmunity in melanoma patients: a comparison between those exposed and non-exposed to immune checkpoint inhibitors.Journal of neurology · 2024Pooled it
- A real-world decision tree model based on ¹⁸F-FDG PET/CT Z scores and Mini-Mental State Examination for differentiating mild cognitive impairment from clinically diagnosed Alzheimer disease.Annals of nuclear medicine · 2026Article
- Artificial Intelligence in Biomedicine: A Systematic Review from Nanomedicine to Neurology and Hepatology.Pharmaceutics · 2025Review
- Diagnostic Accuracy of DaTQUANTBiomedicines · 2025Article
- Artificial Intelligence in PET Imaging for Alzheimer's Disease: A Narrative Review.Brain sciences · 2025Review
- Artificial Intelligence in Alzheimer's Disease Diagnosis and Prognosis Using PET-MRI: A Narrative Review of High-Impact Literature Post-Tauvid Approval.Journal of clinical medicine · 2025Review
- Clinical Performance of Analog and Digital 18F-FDG PET/CT in Pediatric Epileptogenic Zone Localization: Preliminary Results.Biomedicines · 2025Article
- Combined 18F-FDG PET-CT markers in dementia with Lewy bodies.Alzheimer's & dementia (Amsterdam, Netherlands)Article
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Authors and funding
11 authors at 3 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
purposeWe evaluate the ability of Artificial Intelligence with automatic classification methods applied to semi-quantitative data from brain PROCEDURES: We retrospectively analyzed a total of 150 consecutive patients who underwent diagnostic evaluation for suspected AD (n = 67) or MCI (n = 83). All patients received brain 18F-FDG PET/CT according to the international guidelines, and images were analyzed both Qualitatively (QL) and Quantitatively (QN), the latter by a fully automated post-processing software that produced a z score metabolic map of 25 anatomically different cortical regions. A subset of n = 122 cases with a confirmed diagnosis of AD (n = 53) or MDI (n = 69) by 18-24-month clinical follow-up was finally included in the study. Univariate analysis and three automated classification models (classification tree -ClT-, ridge classifier -RC- and linear Support Vector Machine -lSVM-) were considered to estimate the ability of the z scores to discriminate between AD and MCI cases in.
resultsThe univariate analysis returned 14 areas where the z scores were significantly different between AD and MCI groups, and the classification accuracy ranged between 74.59% and 76.23%, with ClT and RC providing the best results. The best classification strategy consisted of one single split with a cut-off value of ≈ -2.0 on the z score from temporal lateral left area: cases below this threshold were classified as AD and those above the threshold as MCI.
conclusionsOur findings confirm the usefulness of brain 18F-FDG PET/CT QL and QN analyses in differentiating AD from MCI. Moreover, the combined use of automated classifications models can improve the diagnostic process since its use allows identification of a specific hypometabolic area involved in AD cases in respect to MCI. This data improves the traditional 18F-FDG PET/CT image interpretation and the diagnostic assessment of cognitive disorders.
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