Deep learning-based AI model for sinusitis diagnosis.

Abstract

Problem statement: While CT (Computed Tomography) is commonly used, its diagnostic accuracy for chronic sinusitis remains uncertain. Moreover, the high cost of CT examinations limits its use as a routine diagnostic method. There is an urgent need to develop an AI-assisted diagnostic model for sinusitis.

Objective: The primary aim of this study is to develop an AI-assisted diagnostic model for sinusitis that can improve diagnostic accuracy and accessibility compared to traditional CT methods.

Methodology: This study utilized a retrospective approach, focusing on patients diagnosed with chronic sinusitis via CT and normal patients admitted to the People's Hospital between January 2018 and January 2019. A total of 5000 sinus CT images were collected. All cases underwent T (targeted) coronal plain scans in the hospital's CT room, ensuring complete CT images. In constructing the chronic sinusitis classification model based on deep learning, 5000 CT images of soft tissue windows and sinuses were gathered. This included 1000 CT images for each of the four groups diagnosed with sphenoid sinusitis, frontal sinusitis, ethmoid sinusitis, and maxillary sinusitis, along with 1000 images from normal cases (250 images per group). The sigmoid function replaced the softmax function, and the binary cross-entropy function was used to assess the model's predictive accuracy.

Results: The model achieved an accuracy of 85.8%, outperforming doctors with low (71.7%), medium (78.4%), and senior (73.4%) qualifications. The model demonstrated high accuracy, superior feature extraction, and resolution capabilities.