Results & Discussion : Identification of dental implants using deep learning (3)
To identify implant systems from radiographic images, dental radiography, panoramic radiography, and computed tomography were considered. In this system, it is thought that implant systems are identified by the shape of the collar, groove, and apex of the implant images, which are unique characteristics of each implant. Consequently, the quality of the training images is important so that these shapes of the implants can be recognized in detail. The advantage of using panoramic radiographic images is that they are standardized to a certain level regardless of the patient, and the shapes of the implants in the images are also standardized. However, the disadvantage is that the implant shapes are unclear when they overlap with a shadow, such as the spina or floor of the maxillary sinus, or when they were too short or much inclined. This may cause misdetection, and some misdetections actually occurred in the result of this study (Fig. 6). In such cases, the images of dental radiography may be more useful. Another disadvantage is the shape of the images. The shape of the images in the learning procedure of this algorithm is square, but the original panoramic radiographic images are rectangular. Therefore, in the learning procedure, panoramic radiographic images are laterally compressed and the shapes of the implants are also compressed. As a result, implant details become unclear, and this could decrease learning performance. The learning performance could be increased by cropping the original panoramic image into a square shape that includes implants beforehand.
In this study, four systems by one manufacturer and more two systems by two manufactures were selected. The reason was to know how much of a difference this system could identify. In the results, the misidentification between MK III/IIIG and Genesio often occurred, especially some of Genesio were misidentified as MK III/IIIG. They are all straight type, and the differences among them are subtle: differences among three systems are the shape of the platform and apex. These small differences are not easy to distinguish in compressed images and misidentification hence occurred. Increasing images with high quality must also be a solution to prevent these misidentifications. In addition to the shape of apex or collar, other differences, such as the shape of the inner screw or space between the bottom of the inner screw and implant body, may be helpful to identify similar-shaped implant.
Serial posts:
- Identification of dental implants using deep learning — pilot study
- Background : Identification of dental implants using deep learning
- Method : Identification of dental implants using deep learning
- Results & Discussion : Identification of dental implants using deep learning (1)
- Results & Discussion : Identification of dental implants using deep learning (2)
- Results & Discussion : Identification of dental implants using deep learning (3)
- Conclusion : Identification of dental implants using deep learning
- Figure 1: Sample image for calculating IoU (MK III implant)
- Figure 2. Total number of objects of each implant systemin all images.
- Figure 3. The total number of implant systems detected correctly (TPs)
- Figure 4. Ratio of implant systems detected correctly to all detected systems
- Figure 5. Average precision (AP) of each implant system in all images
- Figure 6. Sample images of misdetected implants