# Deep Learning Based Airway Segmentation Using Key Point Prediction
## 4 Discussion  
In the medical field, many studies have used artificial intelligence via deep learning in radiology [29; 30]. There are studies on fully automated airway segmentation of lungs with volumetric computed tomographic images using a convolutional neural network (CNN) [31] and on automatic segmentation and 3D reconstruction of inferior turbinate and maxillary sinus from otorhinolaryngology [32]. Due to the complex anatomical structure of the airway, there are difficulties in researching the airway using manual measurements, which is a time-consuming process, and entails inter-examiner error, intra-examiner error, and a lack of certainty because of the small differences on a gray scale [23]. For these reasons, automated measurement and analysis are necessary, but the fully auto-segmentation of the airway is challenging and a study of airway segmentation using deep learning in the area of oral and maxillofacial surgery has not previously been reported. Therefore, in this study, we performed a fully automated segmentation of the airway using artificial intelligence for enabling faster and more practical measurement and analysis in clinical practice. The correlation between the coordinates and volumes measured manually and by the deep learning network were evaluated and compared. The distance between the coordinates of each of the five airway reference points was measured between 2.5 mm and 4.1 mm, and the difference between the measured volumes was 48.620 mm\({}^{3}\) in the nasopharynx, 37.987 mm\({}^{3}\) in the oropharynx, and 50.010 mm\({}^{3}\) in the hypopharynx. The difference in total volume was observed to be 85.256 mm\({}^{3}\). Therefore, it is considered that the correlation between each coordinate and volume showed good to excellent reliability. In this study, the threshold is defined by the Otsu method [33], the binarized image is extracted, and deep learning performs fully automatic division of the airway and divides it into the nasopharynx, oropharynx, and hypopharynx parts through the reference plane. The difference between the total volumes in this study was evaluated as an acceptable value at 0.46 cm\({}^{3}\) when compared to the [PERSON] et al. [25] study, which gave the difference between the water volume of an actual prototype and the volume on the CT software as 0.2 cm\({}^{3}\) to 1.0 cm\({}^{3}\). The difference in the volume of the oropharynx was measured as the smallest, which showed the same results as [PERSON] et al. [34]. According to [PERSON] et al. [23], since the oropharynx airway is a completely empty space like a tube, it is straightforward to measure the volume. The more complex and narrow shape of the airway's soft tissue is  
Figure 6: Bland-Altman plot of the total volume data set. The green line indicates the upper limit of agreement, while the red line indicates the lower limit of agreement (N = 61). due to anatomical complexity, such as epiglottis. This has the highest error in volumetric measurements [35]. Therefore, it can be considered that a simpler anatomical structure will result in a smaller difference between the measurement methods. When comparing the distance of each point, the result of this study is not clinically applicable. A clinically acceptable difference between the landmarks is approximately 2 mm, according to [PERSON] et al. [36]. There are several reasons for a possible error, which include the limitation in the number of training data sets and the necessity for more precise data preparation, such as setting more reference points on each slice segmentation. In setting the reference points for precise training, the reference points were selected on the bony parts to reduce the error due to the variety of soft tissue shapes. This allows clear determination of the anatomical point aided by the large difference on a gray scale, and a simpler comparison of the relationship before and after surgery. Hence, this study applied the reference points of the [PERSON] et al. study [28].