Classification of mastoid air cells by CT scan images using deep learning method

Abstract

Purpose Mastoid abnormalities show different types of ear illnesses, however inadequacy of experts and low accuracy of diagnostic demand a new approach to detect these abnormalities and reduce human mistakes. The manual analysis of mastoid CT scans is time-consuming and labor-intensive. In this paper the first and robust deep learning-based approaches is introduced to diagnose mastoid abnormalities using a large database of CT images obtained in the clinical center with remarkable accuracy. Methods In this paper, mastoid abnormalities are classified using the Xception based Convolutional Neural Network (CNN) model, with optimizer Adamax into five categories (Complete pneumatized, Opacification in pneumatization, Partial pneumatization, Opacification in partial pneumatization, None pneumatized). For this reason, a total of 24,800 slides of 152 patients were selected that include the mastoid from most upper to the lowest part of the middle ear cavity to complete the construction of the proposed deep neural network model. Results The proposed model had the best accuracy of 87.80% (based on grader 1) and 88.44% (based on grader 2) on the 20th epoch and 87.70% (based on grader 1) and 87.56% (based on grader 2) on average and also significantly faster than other types of implemented architectures in terms of the computer running time (in seconds). The 99% confidence interval of the average accuracy was 0.012 which means that the true accuracy is 87.80% and 87.56% ± 1.2% that indicates the power of the model. Conclusions The manual analysis of ear cavity CT scans is often time-consuming and prone to errors due to various inter- or intra operator variability studies. The proposed method can be used to automatically analyze the middle ear cavity to classify mastoid abnormalities, which is markedly faster than most types of models with the highest accuracy.

Advances in Image‐Based Artificial Intelligence in Otorhinolaryngology–Head and Neck Surgery: A Systematic Review

“…Some showed the superiority of AI over nonspecialist physicians 26 or otolaryngologists 71 in narrowly defined fields. Some studies used a single AI algorithm, while others combined 2 or more algorithms to solve clinical problems 25,38,63,74,75,79 . The data sources were used to train, validate, and test AI models, including single‐ and multiple‐center databases, 52,81 and public databases 25,85 …”

“…Recent studies have applied AI to otological imaging in various clinical contexts (Supplemental File 3, available online). These studies combined AI with otoscopy, [22][23][24][25][26]31,32,38,48,52,57,68,[74][75][76]79,81,84,85,93,95 computed tomography (CT), 30,36,41,42,50,62,63,70,71,88 and magnetic resonance imaging (MRI). 43,73,78 Most studies have focused on the image-based otoscopic diagnosis and automated segmentation of temporal bone CT for classifying normal and abnormal mastoid air cells.…”

“…Some studies used a single AI algorithm, while others combined 2 or more algorithms to solve clinical problems. 25,38,63,74,75,79 The data sources were used to train, validate, and test AI models, including single-and multiple-center databases, 52,81 and public databases. 25,85 Viscaino et al 26 used 22,000 otoscopic images to train a model that classifies 4 middle and external ear conditions: normal, chronic otitis media, otitis media with effusion, and earwax plugs.…”

A Three-Dimensional and Explainable Artificial Intelligence Model for Evaluation of Chronic Otitis Media Based on Temporal Bone Computed Tomography: Model Development, Validation, and Clinical Application (Preprint)

“…However, these models were primarily based on traditional otoscopic images, which are potentially limited by a narrow field of view and insufficient diagnostic information. Temporal bone CT scans, which are increasingly used in otologic workup by virtue of its accessibility, rich amount of anatomical information and adequate sensitivity in revealing pathological changes, have also been explored in a limited number of studies [22,[42][43][44][45]. Although these AI models demonstrated decent AUROC scores (e.g., >0.9) in common classification tasks, they were all trained to generate predictions based on 2D single-layer CT images.…”