Coronary artery disease is a major cause of morbidity and mortality worldwide. Its underlying histopathology is the atherosclerotic plaque, which comprises lipid, fibrous and—when chronic—calcium components. Intravascular ultrasound (IVUS) and intravascular optical coherence tomography (IVOCT) performed during invasive coronary angiography are reference standards for characterizing the atherosclerotic plaque. Fine image spatial resolution attainable with contemporary coronary computed tomographic angiography (CCTA) has enabled noninvasive plaque assessment, including identifying features associated with vulnerable plaques known to presage acute coronary events. Manual interpretation of IVUS, IVOCT and CCTA images demands scarce physician expertise and high time cost. This has motivated recent research into and development of artificial intelligence (AI)-assisted methods for image processing, feature extraction, plaque identification and characterization. We performed parallel searches of the medical and technical literature from 1995 to 2021 focusing respectively on human plaque characterization using various imaging modalities and the use of AI-assisted computer aided diagnosis (CAD) to detect and classify atherosclerotic plaques, including their composition and the presence of high-risk features denoting vulnerable plaques. A total of 122 publications were selected for evaluation and the analysis was summarized in terms of data sources, methods—machine versus deep learning—and performance metrics. Trends in AI-assisted plaque characterization are detailed and prospective research challenges discussed. Future directions for the development of accurate and efficient CAD systems to characterize plaque noninvasively using CCTA are proposed.

Background: Speckle tracking echocardiography (STE) has emerged as a novel feasible tool for the assessment of left ventricular rotational parameters. Since hypertrophic cardiomyopathy(HCM) shares morphologic features with left ventricular non-compaction (LVNC), we used this imaging modality to compare rotational mechanics between these two entities. Results: We compared global and regional LV function and rotational mechanics between LVNC, HCM, and healthy subjects using STE. Longitudinal strain and torsion were obtained from echocardiographic images from parasternal short axis as well as standard LV apical views. Twelve patients with LVNC [mean age 46.12 ± 14.66 years; median 47.5 IQR (39.25-58.5) years] were compared with 18 HCM patients [mean age 49.48± 17.22 years; median 56 IQR (33-65) years] and 18 healthy subjects [mean age: 51.50± 12.51 years; median 51(45.75-58) years]. LVNC group showed a significantly reduced longitudinal strain at the apical region compared to HCM group (− 12.18 ± 6.25 vs − 18.37 ± 3.67; P < 0.05). Rigid body rotation(RBR) was found in 50% of patients whereas the other half had a normal rotation at the apex and the base. Among the patients with RBR, all patients had a uniform counterclockwise rotation. Conclusion: Longitudinal strain was impaired in both the forms of cardiomyopathy; however, LVNC showed a more significant reduction in the apical region compared to patients with HCM suggesting a development abnormality in these regions. A reduction in left ventricular torsion was specifically noted among patients with LVNC with a uniform anticlockwise rotation of LV base and apex.

PTMC for mitral restenosis in patients with prior surgical valvotomy is as effective as in patients with prior PTMC despite older age, higher NYHA class, higher Wilkins score and atrial fibrillation and can be considered in all patients with restenosis irrespective of the type of past procedures done.