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Research Progress in Intelligent Diagnosis of Vocal Fold Lesions Based on Multimodal Deep Learning: A Narrative Review

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https://doi.org/10.54963/entu.v16i1.2127
Gao, G., Zhao, K., & Liu, M. (2026). Research Progress in Intelligent Diagnosis of Vocal Fold Lesions Based on Multimodal Deep Learning: A Narrative Review. ENT Updates, 16(1), 26–41. https://doi.org/10.54963/entu.v16i1.2127
Volume 16 Issue 1 (2026)
Copyright (c) 2026 Ge Gao, Kai Zhao, Mingbo Liu
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors

  • Ge Gao

    Graduate School, Medical School of Chinese PLA, Beijing 100080, China
  • Kai Zhao

    Department of Otorlaryngology Head and Neck Surgery, Hainan Hospital of Chinese PLA General Hospital, Sanya 572013, China
  • Mingbo Liu

    Department of Otorlaryngology Head and Neck Surgery, Chinese PLA General Hospital, Beijing 100080, China

Received: 19 December 2025; Revised: 9 February 2026; Accepted: 3 March 2026; Published: 12 March 2026

The diagnosis of vocal fold (VF) lesions relies on examinations such as laryngoscopy and voice analysis, which are highly dependent on clinicians' experience. This leads to a relatively higher risk of misdiagnosis and missed diagnosis among junior physicians. In recent years, with the rapid advancement of artificial intelligence (AI), numerous deep learning (DL)-based methods have emerged in this field. Early research primarily focused on single-modality image analysis, such as classifying white light or narrow-band images into benign or malignant lesions using convolutional neural networks (CNNs). However, such methods often fail to fully integrate complementary information from different modalities and fall to meet the clinical demands for multi-classification and risk stratification. Recently, DL and multimodal fusion have gradually become research hotspots. It enables the extraction of complementary multi-category feature information by integrating laryngoscopic images, videos, voice, and clinical text data (e.g., laryngoscopy reports and medical record information), to construct an end-to-end intelligent diagnostic system. This narrative review summarizes the research progress of DL and multimodal fusion in the diagnosis, classification, and severity grading of VF lesions over the past five years (2020–2025). Studies demonstrate that multimodal DL models outperform single-modality models across multiple tasks, which significantly improves the identification and classification accuracy of VF lesions. These models exhibit promising performance. However, DL and multimodal fusion still face numerous challenges, and their clinical translation remains difficult. 

Keywords:

Vocal Fold Multimodal Deep Learning Intelligent Diagnosis Laryngoscopic Images Voice Analysis

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