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Background: Nasal polyps and inverted papillomas often look similar. Clinically, it is difficult to distinguish the masses by endoscopic examination. Therefore, in this study, we aimed to develop a deep learning algorithm for computer-aided diagnosis of nasal endoscopic images, which may provide a more accurate clinical diagnosis before pathologic confirmation of the nasal masses., Methods: By performing deep learning of nasal endoscope images, we evaluated our computer-aided diagnosis system's assessment ability for nasal polyps and inverted papilloma and the feasibility of their clinical application. We used curriculum learning pre-trained with patches of nasal endoscopic images and full-sized images. The proposed model's performance for classifying nasal polyps, inverted papilloma, and normal tissue was analyzed using five-fold cross-validation., Results: The normal scores for our best-performing network were 0.9520 for recall, 0.7900 for precision, 0.8648 for F1-score, 0.97 for the area under the curve, and 0.8273 for accuracy. For nasal polyps, the best performance was 0.8162, 0.8496, 0.8409, 0.89, and 0.8273, respectively, for recall, precision, F1-score, area under the curve, and accuracy. Finally, for inverted papilloma, the best performance was obtained for recall, precision, F1-score, area under the curve, and accuracy values of 0.5172, 0.8125, 0.6122, 0.83, and 0.8273, respectively., Conclusion: Although there were some misclassifications, the results of gradient-weighted class activation mapping were generally consistent with the areas under the curve determined by otolaryngologists. These results suggest that the convolutional neural network is highly reliable in resolving lesion locations in nasal endoscopic images., (© 2024. The Author(s).)

This document is a correction notice for an article titled "Deep learning model for differentiating nasal cavity masses based on nasal endoscopy images" published in BMC Medical Informatics & Decision Making. The correction addresses an error in the funding information provided by the authors. The incorrect funding number is IITP-2023-2020-0-01819, and the correct number is IITP-2024-2020-0-01819. The correction has been made to the original article. The publisher, Springer Nature, maintains a neutral stance on jurisdictional claims and institutional affiliations. The authors of the article are Junhu Tai, Munsoo Han, Bo Yoon Choi, Sung Hoon Kang, Hyeongeun Kim, Jiwon Kwak, Dabin Lee, Tae Hoon Lee, Yongwon Cho, and Tae Hoon Kim. [Extracted from the article]