Application of high resolution computed tomography image assisted classification model of middle ear diseases based on 3D-convolutional neural network.

Journal: Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences
PMID: 36097771

Abstract

OBJECTIVES: Chronic suppurative otitis media (CSOM) and middle ear cholesteatoma (MEC) are the 2 most common chronic middle ear diseases. In the process of diagnosis and treatment, the 2 diseases are prone to misdiagnosis and missed diagnosis due to their similar clinical manifestations. High resolution computed tomography (HRCT) can clearly display the fine anatomical structure of the temporal bone, accurately reflect the middle ear lesions and the extent of the lesions, and has advantages in the differential diagnosis of chronic middle ear diseases. This study aims to develop a deep learning model for automatic information extraction and classification diagnosis of chronic middle ear diseases based on temporal bone HRCT image data to improve the classification and diagnosis efficiency of chronic middle ear diseases in clinical practice and reduce the occurrence of missed diagnosis and misdiagnosis.

Authors

  • Ri Su
    School of Mathematics and Statistics, Central South University, Changsha 410083. suricsu@csu.edu.cn.
  • Jian Song
    School of International Studies, Sun Yat-sen University, Guangzhou, China.
  • Zheng Wang
    Department of Infectious Diseases, Renmin Hospital of Wuhan University, Wuhan 430060, China.
  • Shuang Mao
    Department of Otorhinolaryngology, Xiangya Hospital, Central South University, Changsha 410008.
  • Yitao Mao
    Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, China.
  • Xuewen Wu
    Department of Otorhinolaryngology, Xiangya Hospital, Central South University, Changsha 410008.
  • Muzhou Hou
    School of Mathematics and Statistics, Central South University, Changsha, 410083, China. houmuzhou@sina.com.

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