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Applying Deep Learning with Convolutional Neural Networks to Laryngoscopic Imaging for Real-time Automated Segmentation and Classification of Vocal Cord Leukoplakia
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  • Juanjuan Hu,
  • Jiawei Luo,
  • Jia Ren,
  • Lan Lan,
  • Ying Zhang,
  • dan lu,
  • Xiaobo Zhou,
  • Hui Yang
Juanjuan Hu
West China Hospital/West China School of Medicine, Sichuan University
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Jiawei Luo
West China Hospital/West China School of Medicine, Sichuan University
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Jia Ren
West China Hospital/West China School of Medicine, Sichuan University
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Lan Lan
West China Hospital/West China School of Medicine, Sichuan University
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Ying Zhang
West China Hospital/West China School of Medicine, Sichuan University
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dan lu
West China Hospital/West China School of Medicine, Sichuan University
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Xiaobo Zhou
University of Texas Health Science Center at Houston
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Hui Yang
West China Hospital/West China School of Medicine, Sichuan University
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Abstract

Objectives The study was to apply deep learning (DL) with convolutional neural networks (CNNs) to laryngoscopic imaging for assisting in real-time automated segmentation and classification of vocal cord leukoplakia. Methods This was a single-center retrospective diagnostic study included 216 patients who underwent laryngoscope and pathological examination from October 1, 2018 through October 1, 2019. Lesions were classified as nonsurgical group (NSG) and surgical group (SG) according to pathology. All selected images of vocal cord leukoplakia were annotated independently by 2 expert endoscopists and divided into a training set, a validation set, and a test set in a ratio of 6:2:2 for training the model. Results Among the 260 lesions identified in 216 patients, 2220 images from narrow band imaging (NBI) and 2144 images from white light imaging (WLI) were selected. For segmentation, the average intersection-over-union (IoU) value exceeded 70%. For classification, the model was able to classify the surgical group (SG) by laryngoscope with a sensitivity of 0.93 and specificity of 0.94 in WLI, and a sensitivity of 0.99 and specificity of 0.97 in NBI. Moreover, this model achieved a mean average precision (mAP) of 0.81 in WLI and 0.92 in NBI with an IoU> 0.5. Conclusions The study found that a model developed by applying DL with CNNs to laryngoscopic imaging results in high sensitivity, specificity, and mAP for automated segmentation and classification of vocal cord leukoplakia. This finding shows promise for the application of DL with CNNs in assisting in accurate diagnosis of vocal cord leukoplakia from WLI and NBI.

Peer review status:UNDER REVIEW

18 Jul 2021Submitted to Clinical Otolaryngology
23 Jul 2021Assigned to Editor
23 Jul 2021Submission Checks Completed
10 Aug 2021Reviewer(s) Assigned