TY - JOUR
T1 - Fusion Modeling
T2 - Combining Clinical and Imaging Data to Advance Cardiac Care
AU - Van Assen, Marly
AU - Tariq, Amara
AU - Razavi, Alexander C.
AU - Yang, Carl
AU - Banerjee, Imon
AU - De Cecco, Carlo N.
N1 - Publisher Copyright:
© 2023 Lippincott Williams and Wilkins. All rights reserved.
PY - 2023/12/1
Y1 - 2023/12/1
N2 - In addition to the traditional clinical risk factors, an increasing amount of imaging biomarkers have shown value for cardiovascular risk prediction. Clinical and imaging data are captured from a variety of data sources during multiple patient encounters and are often analyzed independently. Initial studies showed that fusion of both clinical and imaging features results in superior prognostic performance compared with traditional scores. There are different approaches to fusion modeling, combining multiple data resources to optimize predictions, each with its own advantages and disadvantages. However, manual extraction of clinical and imaging data is time and labor intensive and often not feasible in clinical practice. An automated approach for clinical and imaging data extraction is highly desirable. Convolutional neural networks and natural language processing can be utilized for the extraction of electronic medical record data, imaging studies, and free-text data. This review outlines the current status of cardiovascular risk prediction and fusion modeling; and in addition gives an overview of different artificial intelligence approaches to automatically extract data from images and electronic medical records for this purpose.
AB - In addition to the traditional clinical risk factors, an increasing amount of imaging biomarkers have shown value for cardiovascular risk prediction. Clinical and imaging data are captured from a variety of data sources during multiple patient encounters and are often analyzed independently. Initial studies showed that fusion of both clinical and imaging features results in superior prognostic performance compared with traditional scores. There are different approaches to fusion modeling, combining multiple data resources to optimize predictions, each with its own advantages and disadvantages. However, manual extraction of clinical and imaging data is time and labor intensive and often not feasible in clinical practice. An automated approach for clinical and imaging data extraction is highly desirable. Convolutional neural networks and natural language processing can be utilized for the extraction of electronic medical record data, imaging studies, and free-text data. This review outlines the current status of cardiovascular risk prediction and fusion modeling; and in addition gives an overview of different artificial intelligence approaches to automatically extract data from images and electronic medical records for this purpose.
KW - artificial intelligence
KW - big data
KW - cardiac imaging techniques
KW - heart disease risk factors
KW - prognosis
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U2 - 10.1161/CIRCIMAGING.122.014533
DO - 10.1161/CIRCIMAGING.122.014533
M3 - Article
C2 - 38073535
AN - SCOPUS:85180103481
SN - 1941-9651
VL - 16
SP - 986
EP - 997
JO - Circulation: Cardiovascular Imaging
JF - Circulation: Cardiovascular Imaging
IS - 12
ER -