A clinical decision support system using multi-modality imaging data for disease diagnosis

作     者：Gaw, Nathan Schwedt, Todd J. Chong, Catherine D. Wu, Teresa Li, Jing 

作者机构：School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ United States Department of Neurology Mayo Clinic Arizona Phoenix AZ United States 

出 版 物：《IISE Transactions on Healthcare Systems Engineering》 (IISE Transactions Healthc. Syst. Eng.)

年 卷 期：2018年第8卷第1期

页      面：36-46页

学科分类：0832[工学-食品科学与工程（可授工学、农学学位）] 12[管理学] 120202[管理学-企业管理（含：财务管理、市场营销、人力资源管理）] 0202[经济学-应用经济学] 02[经济学] 1004[医学-公共卫生与预防医学(可授医学、理学学位)] 1202[管理学-工商管理] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 1001[医学-基础医学(可授医学、理学学位)] 0837[工学-安全科学与工程] 0823[工学-交通运输工程] 

主　　题：classification Clinical decision support disease diagnosis headache migraine multi-modality imaging particle swarm optimization 

摘      要：Readily available imaging technologies have made it possible to acquire multiple imaging modalities with complementary information for the same patient. These imaging modalities describe different properties about the organ of interest, providing an opportunity for better diagnosis, staging and treatment assessments. However, existing research in combining multi-modality imaging data has not been transformed into a clinical decision support system due to lack of flexibility, accuracy, and interpretability. This article proposes a multi-modality imaging-based diagnostic decision support system (MMI-DDS) that overcomes limitations of existing research. MMI-DDS includes three inter-connected components: (1) a modality-wise principal component analysis (PCA) that reduces data dimensionality and eliminates the need for co-registration of multi-modality images;(2) a novel constrained particle swarm optimization (cPSO) classifier that is built upon the joint set of the principal components (PCs) from all of the imaging modalities;(3) a clinical utility engine that employs inverse operations to identify contributing imaging features (a.k.a. biomarkers) in diagnosing the disease. To validate MMI-DDS, we apply it to a migraine dataset with multi-modality structural and functional magnetic resonance imaging (MRI) data. MMI-DDS shows significantly improved diagnostic accuracy than using single imaging modalities alone and also identifies biomarkers that are consistent with findings in migraine literature. © 2018 “IISE.