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Diagnosis of pharyngeal cancer on endoscopic video images by Mask region‐based convolutional neural network
Author(s) -
Kono Mitsuhiro,
Ishihara Ryu,
Kato Yusuke,
Miyake Muneaki,
Shoji Ayaka,
Inoue Takahiro,
Matsueda Katsunori,
Waki Kotaro,
Fukuda Hiromu,
Shimamoto Yusaku,
Fujiwara Yasuhiro,
Tada Tomohiro
Publication year - 2021
Publication title -
digestive endoscopy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.5
H-Index - 56
eISSN - 1443-1661
pISSN - 0915-5635
DOI - 10.1111/den.13800
Subject(s) - pharynx , medicine , convolutional neural network , artificial intelligence , cancer , radiology , computer science , surgery
Objectives We aimed to develop an artificial intelligence (AI) system for the real‐time diagnosis of pharyngeal cancers. Methods Endoscopic video images and still images of pharyngeal cancer treated in our facility were collected. A total of 4559 images of pathologically proven pharyngeal cancer (1243 using white light imaging and 3316 using narrow‐band imaging/blue laser imaging) from 276 patients were used as a training dataset. The AI system used a convolutional neural network (CNN) model typical of the type used to analyze visual imagery. Supervised learning was used to train the CNN. The AI system was evaluated using an independent validation dataset of 25 video images of pharyngeal cancer and 36 video images of normal pharynx taken at our hospital. Results The AI system diagnosed 23/25 (92%) pharyngeal cancers as cancers and 17/36 (47%) non‐cancers as non‐cancers. The transaction speed of the AI system was 0.03 s per image, which meets the required speed for real‐time diagnosis. The sensitivity, specificity, and accuracy for the detection of cancer were 92%, 47%, and 66% respectively. Conclusions Our single‐institution study showed that our AI system for diagnosing cancers of the pharyngeal region had promising performance with high sensitivity and acceptable specificity. Further training and improvement of the system are required with a larger dataset including multiple centers.