Impact of Artificial Intelligence-Enhanced Optical Coherence Tomography Software on Percutaneous Coronary Intervention Decisions
Bibliographic record
Abstract
Background Integration of intravascular imaging into percutaneous coronary intervention (PCI) workflow demands physician time and expertise. Artificial intelligence (AI)-enabled software that automates the identification of key intravascular imaging parameters has the potential to streamline physician workflow, increase accuracy, and reduce variability in PCI planning decisions. This study investigated if AI-enabled software, Ultreon (Abbott), compared with traditional software, AptiVue (Abbott), improved physician decision-making accuracy, variability, and efficiency in optical coherence tomography (OCT)-based PCI planning. Methods In this multireader, multicase study, 30 interventional cardiologists of varying OCT imaging experience evaluated 21 pre-PCI OCT pullbacks using both Ultreon and AptiVue platforms. Physician PCI planning decisions about lesion morphology, length, and diameter were compared to published best practices. Decision accuracy, variability, and time efficiency were assessed using statistical models. Results Physician OCT-based planning decisions were more accurate using Ultreon compared to AptiVue in the identification of calcium severity by 1.77 (95% CI, 1.27-2.50; P < .001), vessel preparation strategy by 2.00 (95% CI, 1.12-3.4; P = .018), and stent diameter by 2.83 (95% CI, 1.79-4.50; P < .001). Physicians exhibited less variability in assessments using Ultreon, especially for distal and proximal stent landing zone, and planned stent length ( P < .0001). The efficiency of OCT assessments was improved with Ultreon, reducing the duration of OCT assessments by 0.5 minutes ( P < .0001). The benefits were observed irrespective of the physician's prior OCT experience. Conclusions Physician OCT-based PCI planning decisions were more accurate, less variable, and more efficient with AI-enhanced Ultreon software. This could potentially aid in the fuller adoption of intravascular imaging in PCI workflow.
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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 | 0.016 | 0.159 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".