ArticleScientific reports2025
Comparison of conventional and radiomics-based analysis of myocardial infarction using multimodal non-linear optical microscopy.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
Myocardial infarction, a leading cause of mortality worldwide, leaves survivors at significant risk of recurrence caused by scar-related re-entrant ventricular tachyarrhythmias. Effective treatment with ablation therapy requires a precise guidance system. Non-linear optical microscopy techniques, such as second harmonic generation (SHG) and two-photon excited fluorescence (TPEF), are promising candidates for a high-resolution alternative to conventional electrical mapping for assessing infarcted cardiac tissue. Here, we apply SHG and TPEF with a resolution advantage over commonly used electrical mapping techniques to assess ex-vivo sheep heart infarction. Analyzing conventional and radiomic features allows for quantitative characterization of scar tissue. Our machine learning classifier achieved high accuracy, offering a promising, data-driven approach for guiding in-situ ablation therapy with increased precision. This study represents a significant step towards integrating quantitative image analysis in therapeutic interventions.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.