LB-001 Clinical characterization of cerebrovascular disease with neuro optical coherence tomography (<i>n</i>OCT)
Bibliographic record
Abstract
Objectives We present the clinical imaging of cerebrovascular disease using high-resolution, intravascular neuro optical coherence tomography (nOCT). Background Endovascular procedures are increasingly adopted for treating stroke and cerebral artery disease that rely on sophisticated angiographical imaging techniques. However, current angiography modalities face challenges due to limited spatial and contrast resolution. Achieving a more precise visualization of the arterial wall, pathologies, and devices is crucial for better diagnostics during neurovascular procedures. Methods In a feasibility study involving 32 patients undergoing neurovascular procedures, we investigated the safety and efficacy of neuro optical coherence tomography (nOCT) (Pereira, Lylyk et al. 2024, Sci Transl Med16(747): eadl4497). nOCT uses a flexible, wire-like, miniaturized imaging probe with an outer diameter of 0.015’, compatible for delivery through 0.021’ microcatheters used in routine clinical practice. The probe is equipped with high-speed rotating optics and acquires 250 nOCT cross-sectional images per second at a resolution approaching 10 µm. The probe automatically scans up to 75 mm long arterial segments in 2 seconds generating comprehensive, three-dimensional, high-resolution data sets. Data are collected during a brief injection of contrast media, similar to rotational angiography. Results High-quality nOCT images (75 acquisitions) were obtained in all patients, covering 57 unique arteries in both anterior and posterior circulation of the brain, including distal segments of the MCA and the posterior cerebral artery. We utilized nOCT to evaluate various pathologies, such as brain aneurysms, large vessel occlusions causing ischemic stroke, arterial stenoses, dissections, and intracranial atherosclerotic disease. We captured arterial segments ~47 mm in length (average), with excellent image clarity. Contrast injections of ~16-18 ml were employed to obtain good quality data. We observed consistently high image quality across patients with varying levels of tortuosity, including cases with severe tortuosity (figure 1). nOCT revealed disease characteristics such as aneurysm dome shape and wall thickness, as well as atherosclerotic plaques and arterial wall disease and small clots, which are not available through X-ray imaging techniques. Similarly, nOCT provided detailed characterization of implantable devices (flow diverters, stents, and intrasaccular devices) in high resolution, along with insights into healing progression (i.e., neointimal tissue growth) and their interaction with the arterial wall. Conclusion nOCT offered artifact-free, high-resolution visualizations of intracranial artery pathology and neurovascular devices, providing insights not achievable with existing modalities, which may inform a more effective patient treatment and management strategies. Reference From Vitor M. Pereira et al., DOI:10.1126/scitranslmed.adl4497. Reprinted with permission from AAAS. Disclosures V. Pereira: None. N. Cancelliere: None. P. Lylyk: None. I. Lylyk: None. V. Anagnostakou: None. C. Bleise: None. H. Nishi: None. M. Epshtein: None. R. Kind: None. M. Shazeeb: None. A. Puri: None. C. Liang: None. R. Hanel: None. J. Spears: None. T. Marotta: None. D. Lopes: None. G. Ughi: 5; C; Spryte Medical. M. Gounis: 2; C; Spryte Medical. P. Lylyk: None.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".