PurposeDeep learning, a subset of machine learning, is widely utilized in medical
applications. Identifying maxillary sinus pathologies before surgical interventions
is crucial for ensuring successful treatment outcomes. Cone beam computed tomography
(CBCT) is commonly employed for maxillary sinus evaluations due to its high resolution
and lower radiation exposure. This study aims to assess the accuracy of artificial
intelligence (AI) algorithms in detecting maxillary sinus pathologies from CBCT scans.MethodsA
dataset comprising 1000 maxillary sinuses (MS) from 500 patients was analyzed using
CBCT. Sinuses were categorized based on the presence or absence of pathology, followed
by segmentation of the maxillary sinus. Manual segmentation masks were generated using
the semiautomatic software ITK-SNAP, which served as a reference for comparison. A
convolutional neural network (CNN)-based machine learning model was then implemented
to automatically segment maxillary sinus pathologies from CBCT images. To evaluate
segmentation accuracy, metrics such as the Dice similarity coefficient (DSC) and intersection
over union (IoU) were utilized by comparing AI-generated results with human-generated
segmentations.ResultsThe automated segmentation model achieved a Dice score of 0.923,
a recall of 0.979, an IoU of 0.887, an F1 score of 0.970, and a precision of 0.963.ConclusionThis
study successfully developed an AI-driven approach for segmenting maxillary sinus
pathologies in CBCT images. The findings highlight the potential of this method for
rapid and accurate clinical assessment of maxillary sinus conditions using CBCT imaging.
