The Role of PET Detection of Biomarkers in Early Diagnosis, Progression, and Prognosis of Alzheimer’s Disease: A Systematic Review
Bibliographic record
Abstract
Alzheimer's disease (AD) is a chronic neurologic disease characterized by the deposition of Aβ amyloid and tau protein in the neural tissue, which leads to gradual and irreversible deterioration of memory. Positron emission tomography (PET) showed high potential in diagnosing AD. It provided a unique opportunity to assess cerebral amyloid plaques and tau neurofibrillary tangle deposits in the brain tissue without invasive procedures in vivo. Many studies have been focused on PET diagnosis of AD in recent years, which has significantly improved diagnosis and treatment strategies. This review study aims to summarize the role and emphasize the benefits of PET detection of AD biomarkers in early stages, clinical and histological progression assessment, and predicting AD outcomes. Relevant articles published in the last five years, from September 1, 2019, to October 30, 2024, were searched through authentic databases such as PubMed, PubMed Central, Europe PubMed Central, Science Direct, Cochrane Library, and Google Scholar. In this systematic review, we included articles published in English, with available full text, based on human trials, with relevant information regarding participants who underwent PET of the brain to diagnose AD biomarkers. The study strictly followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines and recommendations. The Joanna Briggs Institute (JBI) critical appraisal methods were used to evaluate all selected cross-sectional research, and the Newcastle-Ottawa Scale (NOS) was used to assess the cohort and longitudinal studies. Eleven relevant articles were included in this systematic review, and 2,203 males and females participated. The study revealed that the detection of beta-amyloid PET showed high-precious results in early diagnosis of AD. The detection of tau protein showed a high potential for estimation of the clinical and histological progression and prognosis of AD in longitudinal studies. Identifying amyloid and tau protein accumulation and glucose metabolism alterations is highly predictive of neurodegeneration in preclinical and mild cognitive impairment stages.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".