Abstract: Systems and methods for detecting features, outputting feature representations, determining measurements, and combinations thereof using machine-learned algorithms are provided. In some embodiments, first sample data from a first characterization modality and second sample data from a second characterization modality are provided to a machine-learned algorithm. Improved feature detection, feature representation, and measurement may be realized by using multimodal data with a machine-learned algorithm. In some embodiments, a first characterization modality is an interferometric modality and a second characterization modality is a spectroscopic modality. A first characterization modality may be optical coherence tomography and a second characterization modality may be a diffuse spectroscopy modality, such as near-infrared spectroscopy. Sample data may be intraluminal and/or vascular data useful in characterizing a vascular system of a subject, such as a human.

Abstract: A method and system for detecting plaque and vessel constriction by processing intracoronary optical coherence tomography (IVOCT) pullback data performed by software executed on a computer. One example method includes inputting IVOCT pullback data from an imaging device, performing full semantic segmentation of the every image of the IVOCT pullback data with a frame-based segmentation module, generating a cross-sectional frame-based image of the every image of the segmented IVOCT pullback data with a cross-sectional display, and determining the presence of plaque and vessel constriction with an automated analysis application analyzing the cross-sectional frame-based images.