Deriving Physiological Information from PET Images Using Machine Learning
Bibliographic record
Abstract
Abstract Machine learning (ML) algorithms have become popular in recent years and have found increasing utility in the field of medical imaging, specifically in positron emission tomography (PET) imaging. The interest in ML in PET imaging for the study of neurodegenerative diseases stems from the potential of these techniques to analyze and predict the physiological parameters of biomarkers such as the total volume of distribution (V $$_{\text {t}}$$ t ) in the organ or a structure of the organ to be explored. In this paper, we investigated whether the V $$_{\text {t}}$$ t of [ $$^{18}$$ 18 F]-FEPPA radiotracer, an indicator of neuroinflammation, could be estimated directly in a non-invasive way, given the activity of the radiotracer in brain tissue. The study used several regression models to predict the [ $$^{18}$$ 18 F]-FEPPA V $$_{\text {t}}$$ t in different brain regions where 31 regions of interest were defined for each of 24 patients with Parkinson disease and 20 healthy subjects, and were used to train four tree-based regression models. The predicted and reference values were compared by Bland-Altman analysis and regression model’s performance was evaluated by the mean absolute error (MAE). The best result was obtained by the XGBoost model with a MAE of 2.6. Bland-Altman analysis results indicate that predicted V $$_{\text {t}}$$ t are in average very close to the reference with a bias of 0.23 "Image missing" 2.82. Significant main effect of genotype on [ $$^{18}$$ 18 F]-FEPPA in both caudate and putamen have been preserved by predicted Vt values (p < 0.05). The results of paired t-test indicate that the difference between predicted and reference V $$_{\text {t}}$$ t is not statistically significant in 6 out of 8 groups. The proposed algorithms provide a non-invasive and efficient tool to predict [ $$^{18}$$ 18 F]-FEPPA V $$_{\text {t}}$$ t values, a hallmark of neuroinflammation that is believed to be a potential trigger for Parkinson’s disease development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".