PuSH - Publication Server of Helmholtz Zentrum München: Roadmap on the use of artificial intelligence for imaging of vulnerable atherosclerotic plaque in coronary arteries.

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Föllmer, B.* ; Williams, M.C.* ; Dey, D.* ; Arbab-Zadeh, A.* ; Maurovich-Horvat, P.* ; Volleberg, R.H.J.A.* ; Rueckert, D.* ; Schnabel, J.A. ; Newby, D.E.* ; Dweck, M.R.* ; Guagliumi, G.* ; Falk, V.* ; Mézquita, A.J.V.* ; Biavati, F.* ; Isgum, I.* ; Dewey, M.*

Roadmap on the use of artificial intelligence for imaging of vulnerable atherosclerotic plaque in coronary arteries.

In: Quantification of Biophysical Parameters in Medical Imaging, Second Edition 2024. 2024. 547-568 (Quantification of Biophysical Parameters in Medical Imaging, Second Edition 2024)

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Artificial intelligence (AI) is likely to revolutionize the way medical images are analysed and has the potential to improve the identification and analysis of vulnerable or high-risk atherosclerotic plaques in coronary arteries, leading to advances in the treatment of coronary artery disease. However, coronary plaque analysis is challenging owing to cardiac and respiratory motion, as well as the small size of cardiovascular structures. Moreover, the analysis of coronary imaging data is time-consuming, can be performed only by clinicians with dedicated cardiovascular imaging training and is subject to considerable interreader and intrareader variability. AI has the potential to improve the assessment of images of vulnerable plaque in coronary arteries but requires robust development, testing and validation. Combining human expertise with AI might facilitate the reliable and valid interpretation of images obtained using CT, MRI, PET, intravascular ultrasonography and optical coherence tomography. In this roadmap, we review existing evidence on the application of AI to the imaging of vulnerable plaque in coronary arteries and provide consensus recommendations developed by an interdisciplinary group of experts on AI and noninvasive and invasive coronary imaging. We also outline future requirements of AI technology to address bias, uncertainty, explainability and generalizability, which are all essential for the acceptance of AI and its clinical utility in handling the anticipated growing volume of coronary imaging procedures.

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