Real-time detection of laryngopharyngeal cancer using an artificial intelligence-assisted system with multimodal data.

Journal: Journal of translational medicine
Published Date: Oct 7, 2023

Abstract

BACKGROUND: Laryngopharyngeal cancer (LPC) includes laryngeal and hypopharyngeal cancer, whose early diagnosis can significantly improve the prognosis and quality of life of patients. Pathological biopsy of suspicious cancerous tissue under the guidance of laryngoscopy is the gold standard for diagnosing LPC. However, this subjective examination largely depends on the skills and experience of laryngologists, which increases the possibility of missed diagnoses and repeated unnecessary biopsies. We aimed to develop and validate a deep convolutional neural network-based Laryngopharyngeal Artificial Intelligence Diagnostic System (LPAIDS) for real-time automatically identifying LPC in both laryngoscopy white-light imaging (WLI) and narrow-band imaging (NBI) images to improve the diagnostic accuracy of LPC by reducing diagnostic variation among on-expert laryngologists.

Authors

  • Yun Li
    School of Public Health, University of Michigan, Ann Arbor, MI, USA.
  • Wenxin Gu
    School of Computer Science and Engineering, Guangdong Province Key Lab of Computational Science, Sun Yat-Sen University, Guangzhou, 510006, Guangdong, China.
  • Huijun Yue
    Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510080, Guangdong, China.
  • Guoqing Lei
    Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510080, Guangdong, China.
  • Wenbin Guo
    Department of Psychiatry of the Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Mental Health Institute of the Second Xiangya Hospital, Central South University, Changsha, Hunan, China; National Clinical Research Center on Mental Disorders, Changsha, Hunan, China; National Technology Institute on Mental Disorders, Changsha, Hunan, China; Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan, China. Electronic address: guowenbin76@163.com.
  • Yihui Wen
    Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510080, Guangdong, China.
  • Haocheng Tang
    Department of Otolaryngology-Head and Neck Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China.
  • Xin Luo
    Department of Pharmacology, The Basic Medical Sciences College of Xinjiang Medical University Urumqi 830054, China.
  • Wenjuan Tu
    Department of Otolaryngology-Head and Neck Surgery, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China.
  • Jin Ye
    Division of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Ruomei Hong
    Department of Otolaryngology-Head and Neck, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China.
  • Qian Cai
    Department of Otolaryngology-Head and Neck, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China.
  • Qingyu Gu
    Department of Otorhinolaryngology-Head and Neck Surgery, The Sixth Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China.
  • Tianrun Liu
    Department of Otorhinolaryngology-Head and Neck Surgery, The Sixth Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China.
  • Beiping Miao
    Department of Otolaryngology-Head and Neck Surgery, Shenzhen Secondary Hospital and First Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong, China.
  • Ruxin Wang
  • Jiangtao Ren
    School of Data and Computer Science, Guangdong Province Key Lab of Computational Science, Sun Yat-Sen University, Guangzhou, Guangdong 510006, PR China. Electronic address: issrjt@mail.sysu.edu.cn.
  • Wenbin Lei
    Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510080, Guangdong, China. leiwb@mail.sysu.edu.cn.

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