Artificial intelligence–driven longitudinal quantification of technetium pyrophosphate uptake in cardiac amyloidosis: Correlation with multimodality imaging and outcomes
Bibliographic record
Abstract
Transthyretin cardiac amyloidosis (ATTR-CM) is an increasingly recognized cause of heart failure (HF) in older adults. Several therapies for ATTR-CM are now available, with more currently in development. As such, there is an increasing need for methods to assess response to therapy. We evaluated the associations between serial 99m-Technetium pyrophosphate ( 99m Tc-PYP) deep learning measurements with changes in other imaging parameters and clinical outcomes. We included patients with a diagnosis of ATTR-CM and at least two 99m Tc-PYP studies followed through the Amyloidosis Program of Calgary. Patients underwent laboratory testing, echocardiography, and cardiovascular magnetic resonance (CMR) unless contraindications were present. 99m Tc-PYP images were quantified using our previously developed deep learning methodology including assessment of cardiac pyrophosphate activity (CPA) and volume of involvement (VOI). In total 85 patients were included, with median population age 79 (interquartile range 72 – 84) and 76 (89%) male patients. In patients on therapy, there was a reduction in VOI (median 100 to 51, p<0.001), CPA (median 165 to 81, p<0.001), native T1 (median 1399 to 1380, p=0.029), and extracellular volume (median 52 to 50, p=0.031) during a median time of 369 days (interquartile range 365 – 516) between scans. There was a modest correlation between change in CPA with change in native T1 (ρ=0.376, p=0.009). After adjusting for age, treatment, and CPA at follow-up, an increase in CPA during follow-up was also associated with increased risk (adjusted HR 2.31 per SD increase, 95% CI 1.28 – 4.17, p=0.005). Serial 99m Tc-PYP quantitation has modest correlations with other measures of disease burden including native T1. Changes in these measures were associated with risk of cardiovascular death or HF hospitalization, suggesting that the serial measurements may be clinically meaningful surrogate endpoints.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".