Abstract 4370080: Detection of indication-relevant and incidental arrhythmias on ambulatory ECG recordings by artificial intelligence compared to ECG technicians
Bibliographic record
Abstract
Background: In the DRAI MARTINI study, the DeepRhythmAI (DRAI) algorithm had a superior sensitivity for arrhythmia detection compared to ECG technicians. However, it has not been reported whether the additional arrhythmias that were detected were directly relevant to the indication for ambulatory monitoring or were incidental. Methods: We included n=14,606 patients with 14±10 days of continuous ambulatory ECG that was analysed beat-to-beat by both DRAI and ECG technicians (n=167). In patients monitored for tachyarrhythmia (known or suspected atrial fibrillation (AF), palpitations, or transient ischemic attack or stroke) AF, supraventricular tachycardias (SVTs), ectopic atrial rhythm (EAR), ventricular tachycardia (VT) or idioventricular rhythm (IVR) were considered relevant findings, while 2 nd or 3 rd degree atrioventricular block (AVB) and pauses/asystoles >2.0/3.5s incidental. In patients monitored for bradycardia (syncope or dizziness) any finding of AVB, pause/asystole, VT, and IVR were considered relevant, whereas EAR, SVT and AF incidental. DRAI and technicians were compared to annotations by a panel of three experts, and confidence intervals (CIs) were derived using bootstrapping with 1,000 replications. Results: The sensitivity for both monitoring indications (tachyarrhythmia and bradycardia) was superior for DRAI compared to technicians. In patients monitored for tachyarrhythmia, the sensitivity for relevant arrhythmias was 99.5% (95%CI 98.8-100.0%) for AI vs. 67.9% (95%CI 62.9-71.8%) for technicians. The corresponding rates of arrhythmia detection were 221/1,000 patient-recordings (95%CI 209-234) for AI vs. 142/1000 patient-recordings (95%CI 131-153) for technicians. In patients monitored for bradycardia, the sensitivity for relevant arrhythmias was 99.4% for DRAI vs 54.6% (95%CI 45.3-61.9%) for technicians, and the corresponding rates of arrhythmia detection 115/1,000 patient-recordings (95%CI 104-126) vs 64/1,000 patient-recordings (95%CI 57-71). DRAI also had higher sensitivity for incidental findings, 98.5% (95%CI 96.3-100%) vs 49.2% (95%CI 41.3-56.2%) in patients monitored for tachyarrhythmias and 99.7% (95%CI 99.3-99.9%) vs 77.2% (95%CI 65.8-86.5%) in patients monitored for bradycardia. True positive rates for relevant and incidental findings are shown in Figure 1a-d. Conclusion: Analysis with DRAI has a superior sensitivity compared to technicians both for arrhythmias relevant to the monitoring indication and for incidental findings.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".