ArticleFrontiers in cardiovascular medicine2022
Machine learning assisted reflectance spectral characterisation of coronary thrombi correlates with microvascular injury in patients with ST-segment elevation acute coronary syndrome.
Article in Frontiers in cardiovascular medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 6 citations in OpenAlex.
- Increased Thrombogenicity is Associated With Coronary Microvascular Dysfunction in Patients With STEMI-A Proof-of-Concept Study.Catheterization and cardiovascular interventions : official journal of the Society for Cardiac Angiography & Interventions · 2025Article
- Advancing hyperspectral imaging techniques for root systems: a new pipeline for macro- and microscale image acquisition and classification.Plant methods · 2024Article
- Interventional thrombus modification in STEMI.Nature reviews. Cardiology · 2024Article
- Treatment and Care of Patients with ST-Segment Elevation Myocardial Infarction-What Challenges Remain after Three Decades of Primary Percutaneous Coronary Intervention?Journal of clinical medicine · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aims: We set out to further develop reflectance spectroscopy for the characterisation and quantification of coronary thrombi. Additionally, we explore the potential of our approach for use as a risk stratification tool by exploring the relation of reflectance spectra to indices of coronary microvascular injury. Methods and results: We performed hyperspectral imaging of coronary thrombi aspirated from 306 patients presenting with ST-segment elevation acute coronary syndrome (STEACS). Spatially resolved reflected light spectra were analysed using unsupervised machine learning approaches. Invasive [index of coronary microvascular resistance (IMR)] and non-invasive [microvascular obstruction (MVO) at cardiac magnetic resonance imaging] indices of coronary microvascular injury were measured in a sub-cohort of 36 patients. The derived spectral signatures of coronary thrombi were correlated with both invasive and non-invasive indices of coronary microvascular injury. Successful machine-learning-based classification of the various thrombus image components, including differentiation between blood and thrombus, was achieved when classifying the pixel spectra into 11 groups. Fitting of the spectra to basis spectra recorded for separated blood components confirmed excellent correlation with visually inspected thrombi. In the 36 patients who underwent successful thrombectomy, spectral signatures were found to correlate well with the index of microcirculatory resistance and microvascular obstruction; Conclusion: Machine learning assisted reflectance spectral analysis can provide a measure of thrombus composition and evaluate coronary microvascular injury in patients with STEACS. Future work will further validate its deployment as a point-of-care diagnostic and risk stratification tool for STEACS care.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.