Abstract
The purpose of this study was to investigate the accuracy of the airway volume measurement by a Regression Neural Network-based deep-learning model. A set of manually outlined airway data was set to build the algorithm for fully automatic segmentation of a deep learning process. Manual landmarks of the airway were determined by one examiner using a mid-sagittal plane of cone-beam computed tomography (CBCT) images of 315 patients. Clinical dataset-based training with data augmentation was conducted. Based on the annotated landmarks, the airway passage was measured and segmented. The accuracy of our model was confirmed by measuring the following between the examiner and the program: (1) a difference in volume of nasopharynx, oropharynx, and hypopharynx, and (2) the Euclidean distance. For the agreement analysis, 61 samples were extracted and compared. The correlation test showed a range of good to excellent reliability. A difference between volumes were analyzed using regression analysis. The slope of the two measurements was close to 1 and showed a linear regression correlation (r2 = 0.975, slope = 1.02, p < 0.001). These results indicate that fully automatic segmentation of the airway is possible by training via deep learning of artificial intelligence. Additionally, a high correlation between manual data and deep learning data was estimated.
Highlights
Recently, artificial intelligence has been used in the medical field to predict risk factors through correlation analysis and genomic analyses, phenotype-genotype association studies, and automated medical image analysis [1]
Since the convolutional neural network (CNN) based on artificial neural networks has begun to be used in medical image analysis, research on various diseases is rapidly increasing [2,3]
Oropharynx, hypopharynx, total volume, PNS(y), CV1(y), CV2(x), and CV4(x) indicated excellent reliability, and all other variables indicated good reliability based on the Koo et al report [40]. These results indicate that fully automatic segmentation of the airway is possible through training via deep learning of artificial intelligence
Summary
Artificial intelligence has been used in the medical field to predict risk factors through correlation analysis and genomic analyses, phenotype-genotype association studies, and automated medical image analysis [1]. The use of deep learning in the medical field helps diagnose and treat diseases by extracting and analyzing medical images, and its effectiveness has been proven [4]. Studies related to deep learning in the areas of oral and maxillofacial surgery are limited [5]. For oral and maxillofacial surgery, radiology is used as an important evaluation criterion in the diagnosis of diseases, treatment plans, and follow-up after treatment. The evaluation process is performed manually and the assessment can be different among examiners, or even with the same examiner. This may result in an inefficient and time-consuming procedure [6]. Airway analysis is essential for diagnosis and assessment of the treatment progress of obstructive sleep apnea patients and for predicting the tendency of airway changes after orthognathic surgery [10,11,12,13,14,15,16,17,18,19,20,21]
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