Automated quantification of 99mTc sestamibi myocardial perfusion compared with visual analysis
Bibliographic record
Abstract
OBJECTIVES: The visual interpretation of 99mTc sestamibi single photon emission computed tomography (SPECT) myocardial perfusion images can be challenging due to the quantity of scan information generated, the large number of normal variants, attenuation artifacts and gender differences. The development of automated, computer derived, quantitative indices of perfusion can assist in this interpretation by providing an objective measure. It is important to verify that similar results can be obtained when the software is used in centres outside those where the algorithms were initially developed. Our objective was to assess the degree of concordance between the visual and automated diagnostic assessments of 99mTc sestamibi SPECT. METHODS: We studied 718 patients referred for 99mTc sestamibi SPECT myocardial perfusion imaging. The SPECT studies were initially interpreted visually without benefit of computer based analysis, and were then subjected to blinded reprocessing to extract quantitative indices of perfusion. RESULTS: There was very good agreement between the visual and quantitative diagnostic classifications. When a visual abnormality was taken to be the reference standard, the automated summed stress score (SSS) showed agreement (SSS>3) in 80% (kappa 0.60, P<0.0001). The area under the receiver operating characteristic (ROC) curve was 0.89 (95% confidence interval (CI), 0.86-0.91). Concordance was greater in those with previous myocardial infarction or severe perfusion defects, but was not affected by age, prior revascularization, stress procedure or heart rate. Concordance over the presence or absence of visual reversibility and the summed difference score (SDS) in abnormal scans was slightly lower (overall agreement 73% (kappa 0.36, P<0.00001) and ROC area 0.84 (95% CI, 0.77-0.90)). CONCLUSION: Automated quantification of 99mTc sestamibi SPECT myocardial perfusion with the SSS and SDS provides objective diagnostic information and concordance when compared with conventional visual image interpretation.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".