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Deep learning model for differentiating nasal cavity masses based on nasal endoscopy imagesopen access

Authors
Tai, JunhuHan, MunsooChoi, Bo YoonKang, Sung HoonKim, HyeongeunKwak, JiwonLee, DabinLee, Tae HoonCho, YongwonKim, Tae Hoon
Issue Date
May-2024
Publisher
BioMed Central
Keywords
Deep learning; Artificial intelligence; Nasal endoscopy; Nasal polyps; Inverted papilloma
Citation
BMC Medical Informatics and Decision Making, v.24, no.1
Indexed
SCIE
SCOPUS
Journal Title
BMC Medical Informatics and Decision Making
Volume
24
Number
1
URI
https://scholarworks.korea.ac.kr/kumedicine/handle/2021.sw.kumedicine/67000
DOI
10.1186/s12911-024-02517-z
ISSN
1472-6947
Abstract
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.
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Kim, Tae Hoon
Anam Hospital (Department of Otorhinolaryngology-Head and Neck Surgery, Anam Hospital)
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