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. 2023 Jan;37(1):6-12.
doi: 10.13201/j.issn.2096-7993.2023.01.002.

[Automatic anatomical site recognition of laryngoscopic images using convolutional neural network]

[Article in Chinese]
Meiling Wang  1 , Jiqing Zhu  2 , Ying Li  1 , Chengwei Tie  2 , Shixu Wang  2 , Wei Zhang  1 , Guiqi Wang  2 , Xiaoguang Ni  2
Affiliations

[Automatic anatomical site recognition of laryngoscopic images using convolutional neural network]

[Article in Chinese]
Meiling Wang et al. Lin Chuang Er Bi Yan Hou Tou Jing Wai Ke Za Zhi. 2023 Jan.

Abstract
in English, Chinese

Objective:To explore the automatic recognition and classification of 20 anatomical sites in laryngoscopy by an artificial intelligence（AI） quality control system using convolutional neural network（CNN）. Methods: Laryngoscopic image data archived from laryngoscopy examinations at the Department of Endoscopy, Cancer Hospital, Chinese Academy of Medical Sciences from January to December 2018 were collected retrospectively, and a CNN model was constructed using Inception-ResNet-V2+SENet. Using 14000 electronic laryngoscope images as the training set, these images were classified into 20 specific anatomical sites including the whole head and neck, and their performance was tested by 2000 laryngoscope images and 10 laryngoscope videos. Results:The average time of the trained CNN model for recognition of each laryngoscopic image was（20.59 ± 1.55） ms, and the overall accuracy of recognition of 20 anatomical sites in laryngoscopic images was 97.75%（1955/2000）, with average sensitivity, specificity, positive predictive value, and negative predictive value of 100%, 99.88%, 97.76%, and 99.88%, respectively. The model had an accuracy of ≥ 99% for the identification of 20 anatomical sites in laryngoscopic videos. Conclusion:This study confirms that the CNN-based AI system can perform accurate and fast classification and identification of anatomical sites in laryngoscopic pictures and videos, which can be used for quality control of photo documentation in laryngoscopy and shows potential application in monitoring the performance of laryngoscopy.

目的：探讨基于卷积神经网络（CNN）构建的人工智能（AI）质控系统对电子喉镜检查中的20个解剖站点的自动识别和分类情况。 方法：回顾性收集中国医学科学院肿瘤医院内镜科2018年1月至12月电子喉镜检查的图像资料，采用Inception-ResNet-V2+SENet模型训练CNN。使用14 000张电子喉镜图像作为训练集，将这些图像分类到包含整个头颈部的20个具体解剖站点，并通过2000张喉镜图像和10个喉镜录像测试其性能。 结果：训练后的CNN模型对每张喉镜图片识别的平均时间为（20.59±1.55） ms，对喉镜图像中20个解剖站点识别的总准确率为97.75%（1955/2000），平均敏感性、特异性、阳性预测值和阴性预测值分别为100%、99.88%、97.76%和99.88%。该模型对喉镜录像中20个解剖站点识别的准确率≥99%。 结论：基于CNN的AI系统可对电子喉镜图片及录像中的解剖部位进行准确、快速的分类识别，可用于喉镜检查中照片文档的质量控制，在监督喉镜检查质量方面表现出应用潜力。.

Keywords: anatomical classification; artificial intelligence; convolutional neural network; laryngoscopy; quality control.

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Conflict of interest statement

The authors of this article and the planning committee members and staff have no relevant financial relationships with commercial interests to disclose.

Figures

图 1
图 1
覆盖整个头颈部的20个喉镜检查图像采集的具体部位及典型图像
图 2
图 2
Inception-ResNet-V2+SENet模型对喉镜图像解剖部位分类识别的流程图
图 3
图 3
CNN模型对电子喉镜图像中20个解剖站点的识别结果
图 4
图 4
CNN模型对电子喉镜图像中6个主要解剖部位的识别结果
图 5
图 5
CNN模型正确和错误分类的图像
图 6
图 6
AI对相似部位的识别情况举例
See this image and copyright information in PMC

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