Accuracy of a Cascade Network for Semi-Supervised Maxillary Sinus Detection and Sinus Cyst Classification.

Journal: Clinical implant dentistry and related research
PMID: 39898709

Abstract

OBJECTIVE: Maxillary sinus mucosal cysts represent prevalent oral and maxillofacial diseases, and their precise diagnosis is essential for surgical planning in maxillary sinus floor elevation. This study aimed to develop a deep learning-based pipeline for the classification of maxillary sinus lesions in cone beam computed tomography (CBCT) images to provide auxiliary support for clinical diagnosis.

Authors

  • Xueqi Guo
    Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
  • Zelun Huang
    Department of Dental Implantation, Affiliated Stomatology Hospital of Guangzhou Medical University, Guangdong Engineering Research Center of Oral Restoration and Reconstruction, Guangzhou Key Laboratory of Basic and Applied Research of Oral Regenerative Medicine, Guangzhou, Guangdong, China.
  • Jieying Huang
    Department of Oral Implantology, School and Hospital of Stomatology, Guangdong Engineering Research Center of Oral Restoration and Reconstruction & Guangzhou Key Laboratory of Basic and Applied Research of Oral Regenerative Medicine, Guangzhou Medical University, Guangzhou, Guangdong, China.
  • Jialing Wei
    Shenzhen Technology University, Shenzhen, Guangdong Province, China.
  • Yongshan Li
    Department of Oral Implantology, School and Hospital of Stomatology, Guangdong Engineering Research Center of Oral Restoration and Reconstruction & Guangzhou Key Laboratory of Basic and Applied Research of Oral Regenerative Medicine, Guangzhou Medical University, Guangzhou, Guangdong, China.
  • Haoran Zheng
    School of Computer Science and Technology, University of Science and Technology of China, Huangshan Road, Hefei, 230026, People's Republic of China. zhulx@mail.ustc.edu.cn.
  • Shiyong Zhao
    Tianjin Horimed Technology Co., Ltd., Tianjin, 300308, People's Republic of China.

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