ArticleNeurobiology of disease2023
Deep learning combining FDG-PET and neurocognitive data accurately predicts MCI conversion to Alzheimer's dementia 3-year post MCI diagnosis.
Article in Neurobiology of disease, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.
- Limited generalizability and high risk of bias in multivariable models predicting conversion risk from mild cognitive impairment to dementia: A systematic review.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Pooled it
- SPECT and PET imaging of Alzheimer's disease revisited: from biomarkers to artificial intelligence-based prediction.Annals of nuclear medicine · 2026Review
- Review of deep learning models for Alzheimer's disease detection: MRI-centric approaches and multimodal extensions.Frontiers in artificial intelligence · 2026Review
- Development and validation comparison of multiple models for perioperative neurocognitive disorders during hip arthroplasty.Scientific reports · 2025Article
- RESIGN: Alzheimer's Disease Detection Using Hybrid Deep Learning based Res-Inception Seg Network.Current Alzheimer research · 2025Article
- Predicting Four-Year's Alzheimer's Disease Onset Using Longitudinal Neurocognitive Tests and MRI Data Using Explainable Deep Convolutional Neural Networks.Journal of Alzheimer's disease : JAD · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
introductionThis study reports a novel deep learning approach to predict mild cognitive impairment (MCI) conversion to Alzheimer's dementia (AD) within three years using whole-brain fluorodeoxyglucose (FDG) positron emission tomography (PET) and cognitive scores (CS).
methodsThis analysis consisted of 150 normal controls (CN), 257 MCI, and 205 AD subjects from ADNI. FDG-PET and CS were obtained at MCI diagnosis to predict AD conversion within three years of MCI diagnosis using convolutional neural networks.
resultsNeurocognitive scores predicted better than FDG-PET per se, but the best model was a combination of FDG-PET, age, and neurocognitive data, yielding an AUC of 0.785 ± 0.096 and a balanced accuracy of 0.733 ± 0.098. Saliency maps highlighted putamen, thalamus, inferior frontal gyrus, parietal operculum, precuneus cortices, calcarine cortices, temporal gyrus, and planum temporale to be important for prediction. DISCUSSION: Deep learning accurately predicts MCI conversion to AD and provides neural correlates of brain regions associated with AD conversion.
Indexed as
Identifiers
What Socratic holds
Registered trials
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 Socratic graph.