A computerized tomography based deep learning diagnostic method of maxillary sinus fungal balls

Background : Traditional diagnostic methods are limited in accuracy when detecting maxillary sinus fungal balls, leading to a higher risk of misdiagnosis or missed diagnosis. This study focuses on a deep learning -based algorithm for assisting in the localization and diagnosis of maxillary sinus fungal balls, addressing the limitations of conventional diagnostic procedures. Materials and Methods : Axial CT imaging data of maxillary sinus were collected from 107 patients, including 47 cases of maxillary sinus fungal balls, 30 cases of other maxillary sinus lesions and 30 cases of healthy maxillary sinus, based on which, a dataset was constructed and a two -stage assisted diagnosis algorithm consisting of a classification and detection model was established. In the first stage, slices containing maxillary sinus were classified and selected. In the second stage, the selected slices were detected to diagnose and localize the fungal ball lesions in the maxillary sinus. Results: The accuracy of the classification model was 92.71%, the mAP and AP50 of the detection model were 0.73 and 0.76, respectively, and the accuracy of the algorithm for the diagnosis of maxillary sinus fungal balls was 84.4%. Conclusion: It is feasible to develop a two -stage auxiliary diagnosis method for maxillary sinus fungal ball based on deep learning.