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Automated detection of glottic laryngeal carcinoma in laryngoscopic images from a multicentre database using a convolutional neural network

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机构： [1]Department of Otolaryngology &Head Neck Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China. [2]School of Medicine, South China University of Technology, Guangzhou, China. [3]Department of Otorhinolaryngology Head and Neck Surgery, Zhongshan Hospital of Traditional Chinese Medicine, Affiliated to Guangzhou University of Chinese Medicine, Guangdong, Zhongshan, Guangdong, China. [4]Department of Otolaryngology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University,The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, China. [5]Department of Otolaryngology, Zhaoqing Gaoyao People's Hospital, Zhaoqing, China. [6]Department of Otolaryngology, The Second People's Hospital of Longgang District, Shenzhen, China. [7]Department of Otolaryngology, Gaozhou People's Hospital, Gaozhou, China. [8]Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa, USA. [9]Division of Otolaryngology-Head and Neck Surgery, Department of Surgery, School of Medicine and Public Health (A.S.), University of Wisconsin-Madison, Madison, Wisconsin, USA.

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Little is known about the efficacy of using artificial intelligence (AI) to identify laryngeal carcinoma from images of vocal lesions taken in different hospitals with multiple laryngoscope systems. This multicentre study aimed to establish an AI system and provide a reliable auxiliary tool to screen for laryngeal carcinoma.Multicentre case-control study.Six tertiary care centres.Laryngoscopy images were collected from 2179 patients with vocal fold lesions.An automatic detection system of laryngeal carcinoma was established and used to distinguish malignant and benign vocal lesions in 2179 laryngoscopy images acquired from 6 hospitals with 5 types of laryngoscopy systems. Pathological examination was the gold standard for identifying malignant and benign vocal lesions.Out of 89 cases in the malignant group, the classifier was able to correctly identify laryngeal carcinoma in 66 patients (74.16%, sensitivity). Out of 640 cases in the benign group, the classifier was able to accurately assess the laryngeal lesion in 503 cases (78.59%, specificity). Furthermore, the region-based convolutional neural network (R-CNN) classifier achieved an overall accuracy of 78.05%, with a 95.63% negative predictive value and a 32.51% positive predictive value for the testing data set.This automatic diagnostic system has the potential to assist clinical laryngeal carcinoma diagnosis which may improve and standardise the diagnostic capacity of laryngologists using different laryngoscopes.© 2023 John Wiley & Sons Ltd.

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出版当年[2022]版：

大类 | 3 区医学

小类 | 3 区耳鼻喉科学

最新[2025]版：

大类 | 4 区医学

小类 | 4 区耳鼻喉科学

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出版当年[2021]版：

Q2 OTORHINOLARYNGOLOGY

最新[2023]版：

Q2 OTORHINOLARYNGOLOGY

影响因子： 1.7 最新[2023版] 2.2 最新五年平均 2.729 出版当年[2021版] 3.085 出版当年五年平均 2.597 出版前一年[2020版] 2.1 出版后一年[2022版]

第一作者：

第一作者机构： [1]Department of Otolaryngology &Head Neck Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China. [2]School of Medicine, South China University of Technology, Guangzhou, China.

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通讯作者：

通讯机构： [1]Department of Otolaryngology &Head Neck Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China. [2]School of Medicine, South China University of Technology, Guangzhou, China.

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