An automated approach for real-time informative frames classification in laryngeal endoscopy using deep learning

• Published in: European archives of oto-rhino-laryngology 2024-08, Vol.281 (8), p.4255-4264
• Main Authors: Baldini, Chiara, Azam, Muhammad Adeel, Sampieri, Claudio, Ioppi, Alessandro, Ruiz-Sevilla, Laura, Vilaseca, Isabel, Alegre, Berta, Tirrito, Alessandro, Pennacchi, Alessia, Peretti, Giorgio, Moccia, Sara, Mattos, Leonardo S.
• Format: Article
• Language: English
• Subjects: Deep Learning; Feasibility Studies; Head and Neck Surgery; Humans; Laryngeal Diseases - diagnosis; Laryngeal Diseases - diagnostic imaging; Laryngology; Laryngoscopy - methods; Medicine; Medicine & Public Health; Neurosurgery; Otorhinolaryngology
• ISSN: 0937-4477; 1434-4726; 1434-4726
• DOI: 10.1007/s00405-024-08676-z

Purpose Informative image selection in laryngoscopy has the potential for improving automatic data extraction alone, for selective data storage and a faster review process, or in combination with other artificial intelligence (AI) detection or diagnosis models. This paper aims to demonstrate the feasibility of AI in providing automatic informative laryngoscopy frame selection also capable of working in real-time providing visual feedback to guide the otolaryngologist during the examination. Methods Several deep learning models were trained and tested on an internal dataset (n = 5147 images) and then tested on an external test set (n = 646 images) composed of both white light and narrow band images. Four videos were used to assess the real-time performance of the best-performing model. Results ResNet-50, pre-trained with the pretext strategy, reached a precision = 95% vs. 97%, recall = 97% vs, 89%, and the F1-score = 96% vs. 93% on the internal and external test set respectively (p = 0.062). The four testing videos are provided in the supplemental materials. Conclusion The deep learning model demonstrated excellent performance in identifying diagnostically relevant frames within laryngoscopic videos. With its solid accuracy and real-time capabilities, the system is promising for its development in a clinical setting, either autonomously for objective quality control or in conjunction with other algorithms within a comprehensive AI toolset aimed at enhancing tumor detection and diagnosis.