An online open-source tool for automated quantification of liver and myocardial iron concentrations by T2* magnetic resonance imaging
Bibliographic record
Abstract
OBJECTIVE: To assess whether an online open-source tool would provide accurate calculations of T2(*) values for iron concentrations in the liver and heart compared with a standard reference software. METHODS: An online open-source tool, written in pure HTML5/Javascript, was tested in 50 patients (age 26.0 ± 18.9 years, 46% males) who underwent T2(*) MRI of the liver and heart for iron overload assessment as part of their routine workup. Automated truncation correction was the default with optional manual adjustment provided if needed. The results were compared against a standard reference measurement using commercial software with manual truncation (CVI(42)(®) v. 5.1; Circle Cardiovascular Imaging; Calgary, AB). RESULTS: The mean liver T2(*) values calculated with the automated tool was 4.3 ms [95% confidence interval (CI) 3.1 to 5.5 ms] vs 4.26 ms using the reference software (95% CI 3.1 to 5.4 ms) without any significant differences (p = 0.71). In the liver, the mean difference was 0.036 ms (95% CI -0.1609 to 0.2329 ms) with a regression correlation coefficient of 0.97. For the heart, the automated T2(*) value was 26.0 ms (95% CI 22.9 to 29.0 ms) vs 25.3 ms (95% CI 22.3 to 28.3 ms), p = 0.28. The mean difference was 0.72 ms (95% CI 0.08191 to 1.3621 ms) with a correlation coefficient of 0.96. CONCLUSION: The automated online tool provides similar T2(*) values for the liver and myocardial iron concentrations as compared with a standard reference software. ADVANCES IN KNOWLEDGE: The online program provides an open-source tool for the calculation of T2(*) values, incorporating an automated correction algorithm in a simple and easy-to-use interface.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".