Since Professor Brånemark’s introduction of the concept of osseointegration in the 1960s through preclinical and clinical studies, implant dentistry has developed rapidly, becoming a common treatment for tooth loss [1
]. Starting from basic machined surface implants, various surface treatment methods, such as resorbable blasting and sandblasted large-grit acid-etching, have been developed, and the threads and platform shapes of implants have continued to evolve with slight improvements [4
]. At present, the survival and success rates of these improved implants are very high in a wide variety of clinical situations, including systemic diseases and cases posing limitations in bone quality and volume at the implantation site [7
]. Thus, dental implants show much better long-term stability compared to conventional fixed partial dentures or removable dental prostheses, with many studies reporting survival rates of more than 95% for dental implants [11
Continued developments in this area have led to the availability of a variety of implant systems in the market in recent years [13
]. Implant systems are selected and placed according to the preferences and familiarity of clinicians, as well as the masticatory force, bone quality, bone volume, and restoration space available in the patient’s tooth loss area [13
]. With time, some of the older implant varieties have been discontinued and their production ceased, while many new types of implants, which are considerably different from the existing implant fixtures, have been introduced by the same company. Moreover, clinicians’ preferences for implant systems change over time. Jokstad et al. [15
] reported the existence of approximately 220 implant brands from 80 companies worldwide. Even so, the number of implant brands in the market has increased since the publication of this study.
These developments are important because as the types of implants being used have changed over time, knowledge about these implant systems and their inter-compatibilities need to be updated for the current generation of working clinicians [19
]. The younger generation of clinicians may lack experience with implant systems used 20 to 30 years ago, and it may be difficult for certain dentists to identify new implant systems simply by viewing the images of the fixtures in radiographs. For this reason, it can be difficult to find the most suitable replacement for a screw even when common complications occur with the implants, such as screw loosening and screw fractures. This could cause many difficulties in clinical situations, requiring new prosthetics to be manufactured. Then, it is possible that implants may no longer be maintained as required because new prostheses may not be available or other complications may arise, although no issues exist with regard to the osseointegration of the implant fixtures and the surrounding alveolar bone. In the absence of other medical records, knowledge about the type of implant would be revealed only by relying on radiographs because most parts of implant fixtures are buried in the alveolar bone, which cannot be observed in oral examination. Thus, radiographic identification of implants is especially important to provide appropriate diagnoses and treatments to patients.
Research has also been conducted to develop and evaluate implant recognition software (IRS) via creation of a database and classification of the features of implant systems fulfilling the same functions [14
]. However, the database lists the characteristics of the implants based on the information provided by the implant manufacturer in the brochure. Therefore, to identify the desired implant, the details in each of the nine drop-down menus, including implant type, thread feature, surface, and collar details, must be entered manually. Moreover, the software cannot directly analyze images.
Artificial intelligence (AI) has come to play a crucial role in healthcare in recent times. In particular, convolutional neural networks (CNNs) are excellent for the detection of breast cancer, skin diseases, and diabetic retinopathy through the study of medical images [21
]. CNN is the most essential algorithm for current deep learning, which is driving AI development in recent years. CNN is particularly useful for finding patterns to recognize objects and scenes in an image. CNN learns directly from the data, using patterns to classify images without the need to manually extract features. [24
] In the dental field, AI is widely used for the detection of dental caries, measurement of alveolar bone loss due to periodontitis, numbering of teeth through tooth shape recognition, and detection of the inferior alveolar nerve [24
Transfer learning with pre-trained networks has been used for high accuracy and generalization. Transfer learning is also effective for applying learned features from large datasets to small datasets to raise their accuracy and performance. In this study, five popular pre-trained networks in the Pareto frontier were applied for implant type classification while considering the accuracy and computational burden [29
]. The Pareto frontier comprises all networks that outperform the other networks on both metrics considered for comparison (in this case, accuracy and prediction time). Deeper networks can generally achieve higher accuracy by learning richer feature representations. However, deep networks such as Xception and DenseNet require larger amounts of computing power and are characterized by longer prediction times when using graphic processing units (GPUs), but this aspect is difficult to comply with in average research and clinical environments.
Therefore, in this research, we find the optimal pre-trained network architecture that satisfies both the accuracy and the computing power requirements for the classification of implant fixture periapical radiograph images. The tested networks were SqueezeNet, GoogLeNet, ResNet-18, MobileNet-v2, and ResNet-50.
Categorizing the implant fixture type in periapical radiographs with high accuracy is important for maintenance purposes, especially in the absence of accurate medical records. However, this exercise has not been attempted owing to difficulties in image processing and feature extraction. This study shows that a deep learning algorithm can solve this problem by learning features in an end-to-end style using training image data and without the need for complicated image preprocessing. Furthermore, the visual interpretations by the trained networks provide a reasonable explanation of the high accuracy achieved by the deep learning models.
The intraoral x-ray was taken with paralleling technique for standardization. However, absolute parallel angle with implant fixture and the sensor is not possible. This minor arbitrary angle between implant fixture and the sensor contributed as data augmentation to achieve high accuracy of our models.
It is generally known that accuracy tends to increase with deep learning, because the deeper the network, the deeper the learning. This trend was not evident in the results of this experiment, and the increase in the number of network layers was not necessarily proportional to the increase in accuracy. This is because ResNet-18 showed little loss of information between layers due to features such as skip connection. Thus, it can be considered that sufficient learning was possible despite the fewer number of layers.
As the number of parameters that can be learned increased, the graph of the ResNet-18 network became saturated once the accuracy increased. SqueezeNet and MobileNet-v2, which are small networks with less than four million parameters, showed accuracies exceeding 96%. This is due to the fact that in periapical x-rays, the number of features required to classify the four implant fixtures may be small, thus, sufficient learning is possible with a relatively small number of parameters. In the case of ResNet-18, the network already showed a test accuracy close to 1, similar to the case of ResNet-50. Therefore, it appears that the current network structure requires approximately up to 10 million features to distinguish the four implant fixtures in periapical x-rays. In the future, this number can be reduced as the network structure evolves.
This study confirmed that a CNN can analyze implant images and automatically classify the four selected implant fixture types with high accuracy even with a relatively small network size and a small number of images. This means that implant classification networks can be easily learned with relatively low computing power and be applied in mobile environments, making them useful and convenient in clinical situations. Moreover, even a small number of radiographs of older implant systems can be used to create a network to distinguish the different types of implants. This may do away with unnecessary treatments and medical expenses caused by not knowing the exact type of implant. The results of this study may also help in the development of decision assistance software using medical images. This study also showed that trained models search for a distinct part of each implant precisely. As per the CAMs of ResNet-50 and SqueezeNet, which showed the best localization, the networks searched for the connection between the fixture and the abutment for the Brånemark implants, for the overall fixation area in the Dentium and Straumann (BL) implants, and for the transgingival portion and fixture connection in the Straumann tissue level implants. All these parts are distinctive of each implant type. These findings prove that the deep learning model can identify the discriminative features of each implant type well. The CAM of each model revealed a slightly different focus, which will likely improve if the number of datasets increases and the accuracy of the model improves.
This study also presents some limitations. First, only four types of implant systems were selected. Since many types of implants currently exist in the market and some have been in use for a long time, clinicians encounter many more types of implants in practice [15
]. To expand the results of this study, it is necessary to build a database by collecting a wide variety of implant fixture images, including those of implant types that are rarely seen. In this study, we implemented a network that can detect and classify implants with high accuracy even with a small number (about 200) of radiographic images by using exhaustive image augmentation. Therefore, it may be not difficult to acquire adequate numbers of radiographs and create a classification network that includes the various implant systems not included in this study.
This study employed images containing only one implant to determine the possibility of distinguishing implant fixtures. However, further research is needed to create a network that can detect implant fixtures using uncropped images, or to apply a technique to detect multiple implants simultaneously using networks such as You-Only-Look-Once (YOLO) and Single Shot multibox Detector (SSD).
In addition, we trained the network using only periapical radiographic images. As clinicians also use panorama radiographs for treatment purposes, it is necessary to construct a network that is able to learn and identify implant types using panorama radiographs.
In the future, the network should be able to not only classify implant types, but also their diameters and lengths. If the length of the implant can be detected automatically, the degree of marginal bone loss around the implant can be easily checked, which in turn can lead to the development of an algorithm to estimate the health and prognosis of the implant as well as diagnose peri-implantitis [39
]. This could lead to the development of a clinically useful diagnostic software for implant-related complications. It is also very important to accurately identify the diameter of the implant, because this parameter is closely related to the implant connection type. Implants can have a variety of connections depending on the diameter, and because they are very different (depending on the implant system), they may have narrow, regular, or wide connections, which are closely related to the component compatibility of the implant prosthesis system [41
]. By developing a network that accurately classifies the diameter of the implant, the implant system can be automatically identified, and it would be possible to know what components should be prepared for repair and maintenance when mechanical complications occur. Clinicians will also be able to obtain information about other implant systems that are compatible with the detected system. Even if it is difficult to obtain information about an implant system currently due to the discontinuation of its production and sales, a system can be established to help clinicians easily procure and respond to the provided information.