Статья

AI in Medical Imaging Informatics: Current Challenges and Future Directions

Andreas S. PanayidesDepartment of Computer Science, University of Cyprus, Nicosia, CyprusAmir A. AminiElectrical and Computer Engineering Department, University of Louisville, Louisville, USANenad FilipovićUniversity of Kragujevac, Kragujevac, SerbiaAshish SharmaEmory University Atlanta, USASotirios A. TsaftarisSchool of Engineering, The University of Edinburgh, U.KAlistair A. YoungDepartment of Anatomy and Medical Imaging, University of Auckland, Auckland, New ZealandDavid J. ForanDepartment of Pathology and Laboratory Medicine, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, USANhan DoU.S. Department of Veterans Affairs Boston Healthcare System, Boston, USASpyretta GolematiMedical School, National and Kapodistrian University of Athens, Athens, GreeceTahsin KurçStony Brook University, Stony Brook, USAKun HuangSchool of Medicine, Regenstrief Institute, Indiana University, USAKonstantina S. NikitaBiomedical Simulations and Imaging Lab, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, GreeceBen P. VeaseyElectrical and Computer Engineering Department, University of Louisville, Louisville, USAMichalis ZervakisTechnical University of Crete, Chania, GreeceJoel SaltzStony Brook University, Stony Brook, USAConstantinos S. PattichisDepartment of Computer Science of the University of Cyprus, Nicosia, Cyprus

2020en

ABI

Аннотация

This paper reviews state-of-the-art research solutions across the spectrum of medical imaging informatics, discusses clinical translation, and provides future directions for advancing clinical practice. More specifically, it summarizes advances in medical imaging acquisition technologies for different modalities, highlighting the necessity for efficient medical data management strategies in the context of AI in big healthcare data analytics. It then provides a synopsis of contemporary and emerging algorithmic methods for disease classification and organ/ tissue segmentation, focusing on AI and deep learning architectures that have already become the de facto approach. The clinical benefits of in-silico modelling advances linked with evolving 3D reconstruction and visualization applications are further documented. Concluding, integrative analytics approaches driven by associate research branches highlighted in this study promise to revolutionize imaging informatics as known today across the healthcare continuum for both radiology and digital pathology applications. The latter, is projected to enable informed, more accurate diagnosis, timely prognosis, and effective treatment planning, underpinning precision medicine.

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Идентификаторы

DOI: 10.1109/jbhi.2020.2991043

Цитирования и источники

Цитирований: 2Использованных источников: 0

Показатели — AkademScholar