Examination of the performance of machine learning-based automated coronary plaque characterization by near-infrared spectroscopy–intravascular ultrasound and optical coherence tomography with histology
Bibliographic record
Abstract
Aims: Near-infrared spectroscopy-intravascular ultrasound (NIRS-IVUS) and optical coherence tomography (OCT) can assess coronary plaque pathology but are limited by time-consuming and expertise-driven image analysis. Recently introduced machine learning (ML)-classifiers have expedited image processing, but their performance in assessing plaque pathology against histological standards remains unclear. The aim of this study is to assess the performance of NIRS-IVUS-ML-based and OCT-ML-based plaque characterization against a histological standard. Methods and results: Matched histological and NIRS-IVUS/OCT frames from human cadaveric hearts were manually annotated and fibrotic (FT), calcific (Ca), and necrotic core (NC) regions of interest (ROIs) were identified. Near-infrared spectroscopy-intravascular ultrasound and OCT frames were processed by their respective ML classifiers to segment and characterize plaque components. The histologically defined ROIs were overlaid onto corresponding NIRS-IVUS/OCT frames and the ML classifier estimations were compared with histology. In total, 131 pairs of NIRS-IVUS/histology and 184 pairs of OCT/histology were included. The agreement of NIRS-IVUS-ML with histology [concordance correlation coefficient (CCC) 0.81 and 0.88] was superior to OCT-ML (CCC 0.64 and 0.73) for the plaque area and burden. Plaque compositional analysis showed a substantial agreement of the NIRS-IVUS-ML with histology for FT, Ca, and NC ROIs (CCC: 0.73, 0.75, and 0.66, respectively) while the agreement of the OCT-ML with histology was 0.42, 0.62, and 0.13, respectively. The overall accuracy of NIRS-IVUS-ML and OCT-ML for characterizing atheroma types was 83% and 72%, respectively. Conclusion: NIRS-IVUS-ML plaque compositional analysis has a good performance in assessing plaque components while OCT-ML performs well for the FT, moderately for the Ca, and has weak performance in detecting NC tissue. This may be attributable to the limitations of standalone intravascular imaging and to the fact that the OCT-ML classifier was trained on human experts rather than histological standards.
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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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".