Τίτλος:
AI in Medical Imaging Informatics: Current Challenges and Future Directions
Γλώσσες Τεκμηρίου:
Αγγλικά
Περίληψη:
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. © 2013 IEEE.
Συγγραφείς:
Panayides, A.S.
Amini, A.
Filipovic, N.D.
Sharma, A.
Tsaftaris, S.A.
Young, A.
Foran, D.
Do, N.
Golemati, S.
Kurc, T.
Huang, K.
Nikita, K.S.
Veasey, B.P.
Zervakis, M.
Saltz, J.H.
Pattichis, C.S.
Περιοδικό:
IEEE Journal of Biomedical and Health Informatics
Εκδότης:
Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Λέξεις-κλειδιά:
Clinical research; Data Analytics; Deep learning; Diagnosis; Health care; Information management; Medical imaging; Three dimensional computer graphics, Algorithmic methods; Clinical translation; Digital pathologies; Disease classification; Imaging informatics; Learning architectures; Medical data management; Visualization application, Medical informatics, fluorodeoxyglucose, breast cancer; cancer growth; cancer prognosis; cancer staging; cancer therapy; clinical data repository; data interoperability; deep learning; diabetic retinopathy; diagnostic imaging; disease classification; echography; electronic health record; fluorescence imaging; fluorescence microscopy; functional connectivity; gamma radiation; glioblastoma; glioma; high frequency ultrasound; human; image analysis; image retrieval; image segmentation; learning algorithm; lung cancer; magnetic resonance elastography; mammography; melanoma; microscopy; nuclear magnetic resonance imaging; nuclear medicine; optical coherence tomography; overall survival; positron emission tomography-computed tomography; progression free survival; radiation dose; Review; single photon emission computed tomography; tissue microarray; treatment planning; tumor microenvironment; ultrasound; World Health Organization; X ray; x-ray computed tomography; artificial intelligence; computer assisted diagnosis; image processing; medical informatics; personalized medicine, Artificial Intelligence; Big Data; Diagnostic Imaging; Humans; Image Interpretation, Computer-Assisted; Image Processing, Computer-Assisted; Medical Informatics; Precision Medicine
DOI:
10.1109/JBHI.2020.2991043
