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

Yan, Peikai1,2; Li, Shaohua3; Zhou, Zhou4; Liu, Qian4; Wu, Jiahui1; Ren, Qingyi1; Chen, Qiuhuan5; Chen, Zhipeng6; Chen, Ze7; Chen, Shaohua1; Scholp, Austin8,9; Jiang, Jack J.9; Kang, Jing1,2; Ge, Pingjiang2

2023

10.1111/coa.14029

CLINICAL OTOLARYNGOLOGY   Impact Factor & Quartile

1749-4478

1749-4486

Objective: 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.Study design: Multicentre case-control study.Setting: Six tertiary care centres.Participants: Laryngoscopy images were collected from 2179 patients with vocal fold lesions.Outcome measures: 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.Results: 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.Conclusion: 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.

artificial intelligence laryngeal carcinoma laryngoscope multicentre study vocal fold lesions

SCI

English

Others

National Natural Science Foundation of China[82000966]

Otorhinolaryngology

Otorhinolaryngology

WOS:000918351500001

WILEY

CLINICAL MEDICINE

Web of Science

1.Southern Med Univ, Guangdong Prov Peoples Hosp, Guangdong Acad Med Sci, Dept Otolaryngol & Head Neck Surg, 106 Zhongshan Second Rd, Guangzhou 510080, Peoples R China
2.South China Univ Technol, Sch Med, Guangzhou, Peoples R China
3.Guangzhou Univ Chinese Med, Zhongshan Hosp Tradit Chinese Med, Dept Otorhinolaryngol Head & Neck Surg, Zhongshan, Guangdong, Peoples R China
4.Southern Univ Sci & Technol, Jinan Univ, Shenzhen Peoples Hosp, Dept Otolaryngol,Affiliated Hosp 1,Clin Med Coll, Shenzhen, Peoples R China
5.Zhaoqing Gaoyao Peoples Hosp, Dept Otolaryngol, Zhaoqing, Peoples R China
6.Second Peoples Hosp Longgang Dist, Dept Otolaryngol, Shenzhen, Peoples R China
7.Gaozhou Peoples Hosp, Dept Otolaryngol, Gaozhou, Peoples R China
8.Univ Iowa, Dept Biomed Engn, Iowa City, IA USA
9.Univ Wisconsin Madison, Sch Med & Publ Hlth AS, Dept Surg, Div Otolaryngol Head & Neck Surg, Madison, WI USA

Yan, Peikai,Li, Shaohua,Zhou, Zhou,et al. Automated detection of glottic laryngeal carcinoma in laryngoscopic images from a multicentre database using a convolutional neural network[J]. CLINICAL OTOLARYNGOLOGY,2023.

Yan, Peikai.,Li, Shaohua.,Zhou, Zhou.,Liu, Qian.,Wu, Jiahui.,...&Ge, Pingjiang.(2023).Automated detection of glottic laryngeal carcinoma in laryngoscopic images from a multicentre database using a convolutional neural network.CLINICAL OTOLARYNGOLOGY.

Yan, Peikai,et al."Automated detection of glottic laryngeal carcinoma in laryngoscopic images from a multicentre database using a convolutional neural network".CLINICAL OTOLARYNGOLOGY (2023).