Application of Artificial Intelligence in Stroboscopic Image Analysis and Laryngeal Lesion Detection: A Review Study
Code: G-1864
Authors: Armin Khademian * ℗, Fatemeh Rashteh, Sara Abedini
Schedule: Not Scheduled!
Tag: Biomedical Signal Processing
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Abstract:
Abstract
Background and aims: Laryngeal disorders such as laryngitis, vocal fold nodules, and vocal fold polyps can cause hoarseness, breathing difficulties, and swallowing problems due to impaired vocal fold function. Early and accurate diagnosis of laryngeal lesions plays a crucial role in the treatment and recovery process. Diagnosing these lesions is challenging due to their diverse appearance, the need for specialized image interpretation, and the similarity of some lesions to each other, sometimes making diagnosis difficult even for experts. Given the application and importance of this issue, this study investigates the benefits of using Artificial Intelligence (AI) in laryngeal image analysis and laryngeal lesion detection. Method: This study was conducted by searching the PubMed database and Google Scholar search engine using the keywords "Stroboscope," "artificial intelligence," "laryngeal lesions," and "effectiveness." Among the relevant articles, 13 articles published between 2021 and 2025 were reviewed. Results: Studies have shown that the use of AI in laryngeal image analysis can play an effective role in early diagnosis of disorders. Additionally, this technology is beneficial in laryngeal examination and screening, especially in areas with limited resources and specialists, and can be used to train students and novice specialists. Conclusion: In general, AI has high potential and accuracy in diagnosing and interpreting laryngeal images, assisting specialists. In some studies, its accuracy has been reported to be higher than human accuracy. With the advancement and improvement of AI, its use by specialists will increase.
Keywords
Voice Disorders, Image Processing, Laryngoscopy
