Synthetic FDG‐PET hypometabolism sensitivity validation in AD
Bibliographic record
Abstract
Abstract Background The availability of 18‐F fluorodeoxyglucose positron emission tomography (FDG‐PET) is not universal. We hypothesized that synthetically generated FDG‐PET images would be as sensitive to detecting the pattern of hypometabolism associated with AD as real images. Method We developed a deep learning‐based method to produce synthetic FDG‐PET images from 1,828 T1‐weighted MRI / real FDG‐PET image pairs from the ADNI dataset, and validated the technique on a further 284 image pairs. The technique generated synthetic FDG‐PET images which were then processed to compare Standardized Uptake Value Ratio (SUVR) with the pons as reference in 81 brain regions as defined in the Desikan‐Killiany‐Tourville and subcortical default FreeSurfer atlases. Result We tested the differences between synthetic and real FDG‐PET on 745 image pairs (205 controls, 365 mild cognitive impairment (MCI) and 175 AD)(Table 1). Correlations in SUVR values between synthetic and real FDG‐PET ranged between weak (r = 0.13) to strong (r = 0.63), with moderate results in key regions for AD (bilateral precuneus, r = 0.43; bilateral posterior cingulate, r = 0.37). There were significant between‐group (control vs MCI and control vs AD) differences in SUVR values for all regions between synthetic and real PET‐FDG (Figure 1) with synthetic FDG‐PET having lower values. Inter‐group effect sizes were not significantly different in the majority of brain regions (76/81)(Figure 2), with similar effect sizes in the right precuneus (synthetic: ‐0.98 vs original: ‐1.37), left (‐0.77 vs ‐1.0459) and right (‐0.80 vs ‐1.04) posterior cingulate, but different for the left precuneus (‐0.91 vs ‐1.33). Conclusion Synthetic images would increase patients’ accessibility to a meaningful modality for disease assessment while decreasing their exposure to radiation and resources in the health care system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".