Figure 6. Sample images of misdetected implants. a Both implants could not be detected because of the shadow of the spina. b Left implant was detected correctly as MK III/IIIG, but the right implant was not detected because of an unclear image
Figure 5. Average precision (AP) of each implant system in all images. MK III/MK III Groovy: MK III/IIIG, MK IV/Speedy Groovy: MK IV/SG, bone level: BL and Genesio Plus ST: Genesio
Figure 4. Ratio of implant systems detected correctly to all detected systems (True Positive ratio). MK III/MK III Groovy: MK III/IIIG, MK IV/Speedy Groovy: MK IV/SG, bone level: BL and Genesio Plus ST: Genesio
Figure 3. The total number of implant systems detected correctly (TPs) and those detected as other prostheses (FPs). MK III/MK III Groovy: MK III/IIIG, MK IV/Speedy Groovy: MK IV/SG, bone level: BL and Genesio Plus ST: Genesio
Figure 2. Total number of objects of each implant systemin all images. MK III/MK III Groovy: MK III/IIIG, MK IV/Speedy Groovy: MK IV/SG, bone level: BL and Genesio Plus ST: Genesio
Figure 1. Sample image for calculating IoU (MK III implant). The light gray square indicates the ground-truth bounding box, and the dark gray square indicates the predicted bounding box. IoU value was calculated that the overlapped area of light gray and dark gray squares was divided by the united area of light gray and dark gray squares.
Conclusion
Though there are several issues that still need to be addressed, implant systems can be identified from panoramic radiographic images using deep learning-based object detection. To increase the learning performance and apply this system in clinical practice, a higher quality and larger number of implant images and images of other implants will be needed in subsequent studies.
...
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 ...
The second system employs nine questions about implant characteristics. The database returns candidate matching implants based on the answers to these questions, and dentists must match them with those of the patient. Both of these systems require dentists to check whether two images of an implant are the same to identify the implant system. In contrast, the system in this study is based on de...
Results
At least 240 instances of each implant system were detected in the panoramic radiographic images: the most common type was MK III/IIIG (1919 instances) and the least common was Genesio (240 instances; Fig. 2). The number of implants detected correctly (True Positive: TP), and those detected as other systems (false positive: FP) are shown in Fig. 3. The number of both TP and FP were the ...
Methods
Data collection
Panoramic radiographs were obtained from patients who received implant treatment in the Department of Prosthodontics, Gerodontology and Oral Rehabilitation at Osaka University Dental Hospital after January 2000. Panoramic radiographs with unknown implants were excluded and totally 1282 images were used to annotate implants. All images were JPEG files that were resized t...
Background
Dental implants were developed in the 1980s, and they are now used for patients with missing teeth globally. Their effect on dental treatment is great, and various improvements in patients’ quality of life have been reported. Implant treatment is no longer unusual for either patients or dentists. However, because more than 30 years have passed since implants were introduced into cl...
Abstract
Background
In some cases, a dentist cannot solve the difficulties a patient has with an implant because the implant system is unknown. Therefore, there is a need for a system for identifying the implant system of a patient from limited data that does not depend on the dentist’s knowledge and experience. The purpose of this study was to identify dental implant systems using a deep le...