Development and Validation of a Deep Learning and Radiomics Combined Model for Differentiating Complicated From Uncomplicated Acute Appendicitis.

Journal: Academic radiology
Published Date:

Abstract

RATIONALE AND OBJECTIVES: This study aimed to develop and validate a deep learning and radiomics combined model for differentiating complicated from uncomplicated acute appendicitis (AA).

Authors

  • Dan Liang
    First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, People's Republic of China (D.L.); Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, People's Republic of China (D.L., Y.L., D.C., A.C., J.D., X.W.).
  • Yaheng Fan
    From the Departments of Radiology (M. Li, C.L., A.L., L.Z., Jinhui Zhou, D.Z., H.C., Y.X., J.W.) and Pathology (Jing Zhou), The Third Affiliated Hospital, Sun Yat-Sen University, No. 600 Tianhe Rd, Guangzhou, Guangdong, 510630, People's Republic of China; Medical AI Laboratory, School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen, People's Republic of China (Y.F., B.H.); Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, People's Republic of China (H.Y.); Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, People's Republic of China (M. Luo); and Department of Clinical Science, Philips Healthcare China, Shanghai, People's Republic of China (X.Y., W.D., Z.Z.).
  • Yinghou Zeng
    Medical AI Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, Guangdong, People's Republic of China (Y.F., Y.Z., Z.Z., B.H.).
  • Hui Zhou
    CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
  • Hong Zhou
    Department of TCM Orthopedics & Traumatology, Gansu province hospital of TCM, Lanzhou, China.
  • Guangming Li
    Xiangyang Central HospitalAffiliated Hospital of Hubei University of Arts and Science Xiangyang 441000 China.
  • Yingying Liang
    Department of Ophthalmology, Guangdong Provincial People's Hospital; Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, China.
  • Zhangnan Zhong
    Medical AI Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, Guangdong, People's Republic of China (Y.F., Y.Z., Z.Z., B.H.).
  • Dandan Chen
    College of Electronic Engineering, Guangxi Normal University, Guilin, Guangxi, China. Electronic address: chenddan0912@163.com.
  • Amei Chen
    Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, People's Republic of China (D.L., Y.L., D.C., A.C., J.D., X.W.).
  • Guanwei Li
    Department of Colorectal & Anal Surgery, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, People's Republic of China (Guanwei Li).
  • Jinhe Deng
    Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, People's Republic of China (D.L., Y.L., D.C., A.C., J.D., X.W.).
  • Bingsheng Huang
    School of Biomedical Engineering, Shenzhen University Health Sciences Center, Shenzhen, Guangdong 518060, P.R.China.
  • Xinhua Wei
    Department of Radiology, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou 510180, China.