# Computer-Assisted Detection of Cemento-Enamel Junction in Intraoral Ultrasonographs
## 4 Discussion
SBB, MBB, and LBB denote the small (31 \(\times\) 41 pixels), medium (51 \(\times\) 71 pixels), and large (81 \(\times\) 101 pixels) bounding boxes, respectively. and adaptive median filtering. Using 120 intraoral ultrasound images from human volunteers as validation samples, the evaluation showed that the proposed method had high conformance with the raters within the clinical limit. The validation result suggests that the MDs of the intermethod (between the proposed method and the raters) were not much different from the manual interrater and intrarater mismatches, i.e., 0.23-0.30 mm versus 0.21-0.31 mm. The average intrarater reliabilities (ICC = 0.976) and interrater reliability (ICC = 0.983) are smaller than the reliabilities between the proposed method and two raters for all small (ICC = 0.990) and medium (ICC = 0.985) bounding box sizes. The MD increases and the reliability decreases with the box size. In practice, the box size is determined by including the anatomical structures of interest in the ultrasound image. The results also showed the contribution of image preprocessing steps in the improvement of the proposed method. The bigger the bounding box, the more likely the additional irrelevant low and high frequency (isolated) structures will be included, which might affect the identification result. Our results have shown that image preprocessing played a role in reducing the impact of those structures, which are prominent in the larger bounding box. The proposed image processing would be quite beneficial for novice clinicians who tend to choose a larger region of interest corresponding to a larger box. The proposed method took around a second to identify the CE), much faster than manual human visualization and interpretation. Previous studies have reported visualization improvements with the segmentation of ultrasound images, including soft tissue [35] and cortical bone [36]. In dentistry, computer-assisted methods have been studied to identify the dental structures in ultrasound images such as gingival sulcus [37] and alveolar bone [20; 21; 38]. However, previous research did not focus on CEJ detection. Dental clinicians are not typically trained in ultrasound image interpretation. Therefore, this algorithm may assist clinicians in identifying CEJ in ultrasound images automatically with limited training. The location of the CEJ is difficult to detect due to the low contrast-to-noise ratio of the ultrasound images. The proposed method could provide a reference check to clarify the ambiguous situation and increase efficiency and productivity. The findings of this study are subject to several limitations. First, this study focused on using ultrasound images of the incisor teeth. This is due to the fact that the size of the transducer restricts its reach intraorally. Research using more ultrasound images of canine, molar, and premolar teeth in other parts of the oral cavity should be performed to further validate the method. Second, though the method has been demonstrated to be feasible, the manual selection of the bounding box depends on the subjective expertise of the examiner, which, in turn, would induce a considerable degree of examiner-dependent bias. Third, this is preliminary work with a small sample size for feasibility. Recent developments in machine learning offer options to automatically select the bounding box, which eliminates subjective bias and can strengthen the validity of our current method [39; 40]. For the first time, the application of machine learning algorithms to detect alveolar bones in ultrasound images was reported by our research group [21; 38]. Deep learning techniques with a very large database to train the convolution neural networks for CEJ detection should be explored in future research.