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Imaging plays a critical role in exploring the pathophysiology and enabling the diagnostics and therapy assessment in carotid artery disease. Ultrasonography, computed tomography, magnetic resonance imaging and nuclear medicine techniques have been used to extract of known characteristics of plaque vulnerability, such as inflammation, intraplaque hemorrhage and high lipid content. Despite the plethora of available techniques, there is still a need for new modalities to better characterize the plaque and provide novel biomarkers that might help to detect the vulnerable plaque early enough and before a stroke occurs. Optoacoustics, by providing a multiscale characterization of the morphology and pathophysiology of the plaque could offer such an option. By visualizing endogenous (e.g., hemoglobin, lipids) and exogenous (e.g., injected dyes) chromophores, optoacoustic technologies have shown great capability in imaging lipids, hemoglobin and inflammation in different applications and settings. Herein, we provide an overview of the main optoacoustic systems and scales of detail that enable imaging of carotid plaques in vitro , in small animals and humans. Finally, we discuss the limitations of this novel set of techniques while investigating their potential to enable a deeper understanding of carotid plaque pathophysiology and possibly improve the diagnostics in future patients with carotid artery disease., Competing Interests: VN discloses financial interests to iThera Medical GmbH, Spear UG, i3 Inc and sThesis GmbH. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2023 Karlas, Fasoula, Kallmayer, Schäffer, Angelis, Katsouli, Reidl, Duelmer, Al Adem, Hadjileontiadis, Eckstein and Ntziachristos.)

Atherosclerotic cardiovascular disease (ASCVD) is the most common cause of death globally. Increasing amounts of highly diverse ASCVD data are becoming available and artificial intelligence (AI) techniques now bear the promise of utilizing them to improve diagnosis, advance understanding of disease pathogenesis, enable outcome prediction, assist with clinical decision making and promote precision medicine approaches. Machine learning (ML) algorithms in particular, are already employed in cardiovascular imaging applications to facilitate automated disease detection and experts believe that ML will transform the field in the coming years. Current review first describes the key concepts of AI applications from a clinical standpoint. We then provide a focused overview of current AI applications in four main ASCVD domains: coronary artery disease (CAD), peripheral arterial disease (PAD), abdominal aortic aneurysm (AAA), and carotid artery disease. For each domain, applications are presented with refer to the primary imaging modality used [e.g., computed tomography (CT) or invasive angiography] and the key aim of the applied AI approaches, which include disease detection, phenotyping, outcome prediction, and assistance with clinical decision making. We conclude with the strengths and limitations of AI applications and provide future perspectives., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Kampaktsis, Emfietzoglou, Al Shehhi, Fasoula, Bakogiannis, Mouselimis, Tsarouchas, Vassilikos, Kallmayer, Eckstein, Hadjileontiadis and Karlas.)

Background: Internal carotid artery stenosis (ICAS) can cause stroke and cognitive decline. Associated hemodynamic impairments, which are most pronounced within individual watershed areas (iWSA) between vascular territories, can be assessed with hemodynamic-oxygenation-sensitive MRI and may help to detect severely affected patients. We aimed to identify the most sensitive parameters and volumes of interest (VOI) to predict high-grade ICAS with random forest machine learning. We hypothesized an increased predictive ability considering iWSAs and a decreased cognitive performance in correctly classified patients., Materials and Methods: Twenty-four patients with asymptomatic, unilateral, high-grade carotid artery stenosis and 24 age-matched healthy controls underwent MRI comprising pseudo-continuous arterial spin labeling (pCASL), breath-holding functional MRI (BH-fMRI), dynamic susceptibility contrast (DSC), T2 and T2 * mapping, MPRAGE and FLAIR. Quantitative maps of eight perfusion, oxygenation and microvascular parameters were obtained. Mean values of respective parameters within and outside of iWSAs split into gray (GM) and white matter (WM) were calculated for both hemispheres and for interhemispheric differences resulting in 96 features. Random forest classifiers were trained on whole GM/WM VOIs, VOIs considering iWSAs and with additional feature selection, respectively., Results: The most sensitive features in decreasing order were time-to-peak (TTP), cerebral blood flow (CBF) and cerebral vascular reactivity (CVR), all of these inside of iWSAs. Applying iWSAs combined with feature selection yielded significantly higher receiver operating characteristics areas under the curve (AUC) than whole GM/WM VOIs (AUC: 0.84 vs. 0.90, p = 0.039). Correctly predicted patients presented with worse cognitive performances than frequently misclassified patients (Trail-making-test B: 152.5s vs. 94.4s, p = 0.034)., Conclusion: Random forest classifiers trained on multiparametric MRI data allow identification of the most relevant parameters and VOIs to predict ICAS, which may improve personalized treatments., Competing Interests: SK was employed by Philips GmbH Market DACH, Hamburg, Germany. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Gleißner, Kaczmarz, Kufer, Schmitzer, Kallmayer, Zimmer, Wiestler, Preibisch and Göttler.)