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 learning method. Methods A dataset of 1282 panoramic radiograph images with implants were used for deep learning. An object detection algorithm (Yolov3) was used to identify the six implant systems by three manufactures. To implement the algorithm, TensorFlow and Keras deep-learning libraries were used. After training was complete, the true positive (TP) ratio and average precision (AP) of each implant system as well as the mean AP (mAP), and mean intersection over union (mIoU) were calculated to evaluate the performance of the model. Results The number of each implant system varied from 240 to 1919. The TP ratio and AP of each implant system varied from 0.50 to 0.82 and from 0.51 to 0.85, respectively. The mAP and mIoU of this model were 0.71 and 0.72, respectively. Conclusions The results of this study suggest that implants can be identified from panoramic radiographic images using deep learning-based object detection. This identification system could help dentists as well as patients suffering from implant problems. However, more images of other implant systems will be necessary to increase the learning performance to apply this system in clinical practice.
Objectives The aim of this study was to evaluate the prevalence of peri‐implant disease and analyze risk indicators in Japanese subjects with ≥3 years of implant function. Material and methods Five hundred and forty‐three subjects treated with 1,613 implants were evaluated. Information was collected about the patients’ physical and dental history, as well as implant details. Peri‐implant evaluation included probing depth, bleeding on probing (BoP), suppuration (Sup), and keratinized tissue width. Bone loss was calculated from intra‐oral radiographs taken after 1 year and more than 3 years of function. Implants were classified into three groups: healthy, peri‐implant mucositis (BoP without bone loss), and peri‐implantitis (BoP and/or Sup with bone loss >1 mm). These data were analyzed by multivariable multinomial logistic regression. Results The prevalence of peri‐implant mucositis and peri‐implantitis at the subject level was 23.9% and 15.8%, respectively. An association was found between peri‐implant mucositis and plaque control record (PCR) >20% and keratinized tissue width <2 mm. Peri‐implantitis was associated with PCR >20%, smoking, insertion in the maxilla, and keratinized tissue width <2 mm. Conclusions Within the limitations of this study, the prevalence of peri‐implant diseases was elucidated in a Japanese population. Peri‐implant mucositis was associated with poor oral hygiene and less keratinized tissue. Poor oral hygiene, smoking, insertion in the maxilla, and less keratinized tissue were risk indicators for peri‐implantitis.
The purpose of this study is to develop a method for recognizing dental prostheses and restorations of teeth using a deep learning. A dataset of 1904 oral photographic images of dental arches (maxilla: 1084 images; mandible: 820 images) was used in the study. A deep-learning method to recognize the 11 types of dental prostheses and restorations was developed using TensorFlow and Keras deep learning libraries. After completion of the learning procedure, the average precision of each prosthesis, mean average precision, and mean intersection over union were used to evaluate learning performance. The average precision of each prosthesis varies from 0.59 to 0.93. The mean average precision and mean intersection over union of this system were 0.80 and 0.76, respectively. More than 80% of metallic dental prostheses were detected correctly, but only 60% of tooth-colored prostheses were detected. The results of this study suggest that dental prostheses and restorations that are metallic in color can be recognized and predicted with high accuracy using deep learning; however, those with tooth color are recognized with moderate accuracy.
Peri-implant diseases are known as undesirable conditions that can occur after implant therapy. Although several risk indicators are becoming clear, the causes of peri-implant diseases have not been completely investigated. The purpose of this review was to summarize the prevalence and risk indicators for peri-implant diseases by referring to current papers from various angles. Many studies have reported the varied prevalence of peri-implant mucositis (23.9%–88.0% at the patient level and 9.7%–81.0% at the implant level) and peri-implantitis (8.9%–45% at the patient level and 4.8%–23.0% at the implant level). Additionally, several studies concluded that poor oral hygiene and lack of regular maintenance were strongly correlated with the development of both peri-implant mucositis and peri-implantitis. Diabetes and a history of periodontitis were revealed as risk indicators for peri-implantitis. However, there was no definitive conclusion about the correlations between peri-implant diseases and other factors such as smoking, the shape of the implant superstructure, and the condition of the keratinized mucosa. Further studies useful for evidence-based decision-making are needed for predictable implant therapy in the long term.
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