A robust and versatile method for the quantification of myocardial infarct size
Bibliographic record
Abstract
In order to determine the size of myocardial perfusion infarcts in clinical SPECT images, we have developed a software package iQuant that eliminates the need for a normal-heart database - a problem that exists in commercial products. Details of the iQuant method are presented in this paper together with results from preliminary tests of accuracy and reproducibility. iQuant uses different count thresholds to define viable myocardial tissue and to provide a rough outline of the complete myocardium. This outline together with the visible thickness of the viable myocardium is used by the operator as a guide to determine the location and size of infarcted tissue. Using noiseless data iQuant is shown to be accurate to within 5%; with more clinically realistic simulated data the measured accuracy is 14%. When testing reproducibility, independent operators consistently produced results within 10% of the truth. Clinical experience was shown to be an important factor in the reproducible accuracy of each individual observer. Measurements to date only involve infarcts considered clinically small, but further work is planned to investigate the reliability of this method over a large range of infarct sizes and locations. Initial analysis shows the iQuant software to be an objective, robust and versatile tool suitable for the determination of myocardial perfusion infarct size in the clinical research environment. Its elimination of the need for a normal-heart database is its main advantages over current, commercially availably software.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".