Bayesian Networks in Radiology
Issued Date
2023-11-01
Resource Type
eISSN
26386100
Scopus ID
2-s2.0-85177823016
Journal Title
Radiology: Artificial Intelligence
Volume
5
Issue
6
Rights Holder(s)
SCOPUS
Bibliographic Citation
Radiology: Artificial Intelligence Vol.5 No.6 (2023)
Suggested Citation
Ma S.X., Dhanaliwala A.H., Rudie J.D., Rauschecker A.M., Roberts-Wolfe D., Haddawy P., Kahn C.E. Bayesian Networks in Radiology. Radiology: Artificial Intelligence Vol.5 No.6 (2023). doi:10.1148/ryai.210187 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/91300
Title
Bayesian Networks in Radiology
Other Contributor(s)
Abstract
A Bayesian network is a graphical model that uses probability theory to represent relationships among its variables. The model is a directed acy-clic graph whose nodes represent variables, such as the presence of a disease or an imaging finding. Connections between nodes express causal influences between variables as probability values. Bayesian networks can learn their structure (nodes and connections) and/or conditional probability values from data. Bayesian networks offer several advantages: (a) they can efficiently perform complex inferences, (b) reason from cause to effect or vice versa, (c) assess counterfactual data, (d) integrate observations with canonical (“textbook”) knowledge, and (e) explain their reasoning. Bayesian networks have been employed in a wide variety of applications in radiology, including diagnosis and treatment plan-ning. Unlike deep learning approaches, Bayesian networks have not been applied to computer vision. However, hybrid artificial intelligence systems have combined deep learning models with Bayesian networks, where the deep learning model identifies findings in medical images and the Bayesian network formulates and explains a diagnosis from those findings. One can apply a Bayesian network’s probabilistic knowledge to integrate clinical and imaging findings to support diagnosis, treatment planning, and clinical decision-making. This article reviews the funda-mental principles of Bayesian networks and summarizes their applications in radiology.