Preliminary study on AI-assisted diagnosis of bone remodeling in chronic maxillary sinusitis

TL;DR

This study explores using AI to improve the diagnosis of chronic maxillary sinusitis and related bone remodeling through CT images.

Contribution

The paper introduces AI models for detecting chronic maxillary sinusitis and its bone remodeling using deep learning and machine learning.

Findings

AI models achieved high sensitivity and accuracy for detecting chronic maxillary sinusitis.

The AUC values for CMS and CMS with bone remodeling were 0.94 and 0.89, respectively.

AI methods show feasibility for standardizing imaging diagnosis in clinical settings.

Abstract

To construct the deep learning convolution neural network (CNN) model and machine learning support vector machine (SVM) model of bone remodeling of chronic maxillary sinusitis (CMS) based on CT image data to improve the accuracy of image diagnosis.

Maxillary sinus CT data of 1000 samples in 500 patients from January 2018 to December 2021 in our hospital was collected. The first part is the establishment and testing of chronic maxillary sinusitis detection model by 461 images. The second part is the establishment and testing of the detection model of chronic maxillary sinusitis with bone remodeling by 802 images. The sensitivity, specificity and accuracy and area under the curve (AUC) value of the test set were recorded, respectively.

Preliminary application results of CT based AI in the diagnosis of chronic maxillary sinusitis and bone remodeling. The sensitivity, specificity and accuracy of the test set of 93 samples of CMS, were 0.9796, 0.8636 and 0.9247, respectively. Simultaneously, the value of AUC was 0.94. And the sensitivity, specificity and accuracy of the test set of 161 samples of CMS with bone remodeling were 0.7353, 0.9685 and 0.9193, respectively. Simultaneously, the value of AUC was 0.89.

It is feasible to use artificial intelligence research methods such as deep learning and machine learning to automatically identify CMS and bone remodeling in MSCT images of paranasal sinuses, which is helpful to standardize imaging diagnosis and meet the needs of clinical application.

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