Hybrid Cardiac Imaging: The Role of Machine Learning and Artificial Intelligence

Jan-Walter Benjamins, Ming Wai Yeung, Alvaro E. Reyes-Quintero, Bram Ruijsink, Pim van der Harst, Luis Juarez-Orozco


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Machine learning currently represents the cornerstone of modern artificial intelligence. The algorithms involved have rapidly permeated into medical sciences and have demonstrated the capacity to revolutionize data analysis through optimized variable exploration and integration as well as improved image processing and recognition. As such, cardiovascular hybrid imaging constitutes an open pathway for implementation in the form of view identification, structure segmentation, disease identification, functional parameter estimation, and prognostic evaluation in its traditional forms in SPECT/CT, PET/CT, and PET/MR. Further, an elastic view of the concept of hybridization in cardiovascular imaging offers the possibility to concatenate applications based on the combination of machine learning models, data types, and imaging modalities. Current aims for these implementations include process automation and the generation of clinical decision support systems tailored to the needs of daily clinical practice in the evaluation of cardiovascular disease at the individual level. This chapter summarizes core concepts in modern machine learning-based AI, provides an overview of the recent advances in data processing, image analysis, result in interpretation and emerging clinical implementations, and suggests the potential and future perspectives of machine learning analytics within the context of hybrid cardiovascular imaging.
Originele taal-2English
TitelHybrid Cardiac Imaging for Clinical Decision-Making
SubtitelFrom Diagnosis to Prognosis
RedacteurenFrancesco Nudi, Orazio Schillaci, Giuseppe Biondi-Zoccai, Ami E. Iskandrian
Plaats van productieCham
UitgeverijSpringer Nature
Aantal pagina's20
ISBN van elektronische versie978-3-030-99391-7
ISBN van geprinte versie978-3-030-99390-0
StatusPublished - 19-aug.-2022

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