Affiliation:
1. Department of Otolaryngology, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine , Changwon 51353, Republic of Korea
2. School of Electronic and Electrical Engineering, Hongik University , Seoul 04066, Republic of Korea
Abstract
Abstract
Background
Sinusitis is a commonly encountered clinical condition that imposes a considerable burden on the healthcare systems. A significant number of maxillary sinus opacifications are diagnosed as sinusitis, often overlooking the precise differentiation between cystic formations and inflammatory sinusitis, resulting in inappropriate clinical treatment. This study aims to improve diagnostic accuracy by investigating the feasibility of differentiating maxillary sinusitis, retention cysts, and normal sinuses.
Methods
We developed a deep learning-based automatic detection model to diagnose maxillary sinusitis using ostiomeatal unit CT images. Of the 1080 randomly selected coronal-view CT images, including 2158 maxillary sinuses, datasets of maxillary sinus lesions comprised 1138 normal sinuses, 366 cysts, and 654 sinusitis based on radiographic findings, and were divided into training (n = 648 CT images), validation (n = 216), and test (n = 216) sets. We utilized a You Only Look Once based model for object detection, enhanced by the transfer learning method. To address the insufficiency of training data, various data augmentation techniques were adopted, thereby improving the model’s robustness.
Results
The trained You Only Look Once version 8 nano model achieved an overall precision of 97.1%, with the following class precisions on the test set: normal = 96.9%, cyst = 95.2%, and sinusitis = 99.2%. With an average F1-score of 95.4%, the F1-score was the highest for normal, then sinusitis, and finally, cysts. Upon evaluating a performance on difficulty level, the precision decreased to 92.4% on challenging test dataset.
Conclusions
The developed model is feasible for assisting clinicians in screening maxillary sinusitis lesions.
Funder
Sungkyunkwan University School of Medicine Samsung Changwon Hospital
National Research Foundation of Korea
Korea government
Publisher
Oxford University Press (OUP)
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