Application of deep convolutional neural networks in the diagnosis of laryngeal squamous cell carcinoma based on narrow band imaging endoscopy.
10.3760/cma.j.cn115330-20200927-00773
- VernacularTitle:基于深度卷积神经网络的人工智能在喉鳞状细胞癌窄带成像辅助诊断中的应用
- Author:
Rong HU
1
;
Qi ZHONG
1
;
Wen XU
1
;
Zhi Gang HUANG
1
;
Li Yu CHENG
1
;
Yuan WANG
1
;
Yu Rong HE
1
;
Ying Duan CHENG
2
Author Information
1. Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Key Laboratory of Otorhinolaryngology Head and Neck Surgery (Capital Medical University), Ministry of Education, Beijing 100730, China.
2. Department of Urology, the First Affiliated Hospital of Southern University of Science and Technology, the Second Clinical Medical College of Jinan University, Shenzhen People's Hospital,Shenzhen 518000, China.
- Publication Type:Journal Article
- MeSH:
Adult;
Aged;
Aged, 80 and over;
Artificial Intelligence;
Endoscopy;
Female;
Head and Neck Neoplasms;
Humans;
Male;
Middle Aged;
Narrow Band Imaging;
Neural Networks, Computer;
Sensitivity and Specificity;
Squamous Cell Carcinoma of Head and Neck;
Young Adult
- From:
Chinese Journal of Otorhinolaryngology Head and Neck Surgery
2021;56(5):454-458
- CountryChina
- Language:Chinese
-
Abstract:
Objective: To explore the possibility of using artificial intelligence (AI) technology based on convolutional neural network (CNN) to assist the clinical diagnosis of laryngeal squamous cell carcinoma (LSCC) through deep learning algorithm. Methods: A deep CNN was developed and applied in narrow band imaging (NBI) endoscopy of 4 799 patients with laryngeal lesions, including 3 168 males and 1 631 females, aged from 21 to 87 years, from 2015 to 2017 in Beijing Tongren Hospital, Capital Medical University. A simple randomization method was used to select the laryngeal NBI images of 2 427 patients (1 388 benign lesions and 1 039 LSCC lesions) for the training and correction the CNN model. The remaining laryngeal NBI images of 2 372 patients (including 1 276 benign lesions and 1 096 LSCC lesions) were used as validation data set to compare performance between CNN and otolaryngologists. SPSS 21.0 software was used for Chi-square test to calculate the accuracy, sensitivity and specificity of AI and otolaryngologists. The area under the curve (AUC) of receiver operating curve (ROC) was used to evaluate the diagnostic ability of the algorithm for NBI images. Results: The accuracy, sensitivity and specificity for NBI predictions were respectively 90.91% (AUC=0.96), 90.12% and 91.53%, which were equivalent to those for otolaryngologists' predictions (accuracy, sensitivity and specificity were (91.93±3.20)%, (91.33±3.25)% and (93.02±2.59)%, t values were 0.64, 0.75 and 1.17, and P values were 0.32, 0.28 and 0.21, respectively). The diagnostic efficiency of CNN was significantly higher than that of otolaryngologists (0.01 vs. 5.50, t =9.15, P<0.001). Conclusion: AI based on deep CNN is effective for using in the laryngeal NBI image diagnosis, showing a good application prospect in the diagnosis of LSCC.
