Predicting the Debonding of CAD/CAM Composite Resin Crowns with AI

Yamaguchi, Satoshi; Lee, C; Karaer, Oğuzcan; Ban, Shintaro; Mine, Atsushi; Imazato, Satoshi

doi:10.1177/0022034519867641

Cited by 86 publications

(70 citation statements)

References 23 publications

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“…Based on patients’ attributes, a multilayer perceptron neural network performed satisfactorily in predicting tooth mobility (Yoon et al 2018) and longevity of resin composite restorations (Aliaga et al 2015). CNNs achieved 98.5% accuracy in predicting the debonding probability of CAD/CAM resin composite crowns by analyzing images captured from abutment models (Yamaguchi et al 2019). These systems are useful in forecasting prognosis and enable dentists to provide patients with the most suitable maintenance program.…”

Section: Applications Of Ai In Dentistrymentioning

confidence: 99%

Application of Artificial Intelligence in Dentistry

2020

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Artificial intelligence (AI) is a technology that utilizes machines to mimic intelligent human behavior. To appreciate human-technology interaction in the clinical setting, augmented intelligence has been proposed as a cognitive extension of AI in health care, emphasizing its assistive and supplementary role to medical professionals. While truly autonomous medical robotic systems are still beyond reach, the virtual component of AI, known as software-type algorithms, is the main component used in dentistry. Because of their powerful capabilities in data analysis, these virtual algorithms are expected to improve the accuracy and efficacy of dental diagnosis, provide visualized anatomic guidance for treatment, simulate and evaluate prospective results, and project the occurrence and prognosis of oral diseases. Potential obstacles in contemporary algorithms that prevent routine implementation of AI include the lack of data curation, sharing, and readability; the inability to illustrate the inner decision-making process; the insufficient power of classical computing; and the neglect of ethical principles in the design of AI frameworks. It is necessary to maintain a proactive attitude toward AI to ensure its affirmative development and promote human-technology rapport to revolutionize dental practice. The present review outlines the progress and potential dental applications of AI in medical-aided diagnosis, treatment, and disease prediction and discusses their data limitations, interpretability, computing power, and ethical considerations, as well as their impact on dentists, with the objective of creating a backdrop for future research in this rapidly expanding arena.

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Section: Applications Of Ai In Dentistrymentioning

confidence: 99%

Application of Artificial Intelligence in Dentistry

2020

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“…A deep learning method, one of the AI technologies, is adequate for prediction, object detection, classification, and other similar tasks. In dentistry, the diagnosis of dental diseases using oral or X-ray images [7], prediction of treatments [8], classification [9], statistics from research data [10], and other topics have been addressed using a deep learning method. Specifically, studies on the diagnosis of diseases using a deep learning have increased, and deep learning-based object detection algorithms for images are usually used for this task [11].…”

Section: Introductionmentioning

confidence: 99%

Identification of dental implants using deep learning—pilot study

et al. 2020

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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.

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“…7 They were used effectively in various fields for image-based automated diagnosis, including lung cancer, 8 colorectal polyps, 9 prostate cancer, 10 hip osteoarthritis, 11 bone age assessment, 12 caries diagnosis, 13 color selection, 14 removable partial denture design, 15,16 temporomandibular disorders, 17,18 orthognathic treatment, 19 maxillary sinusitis, 20 root morphology, 21 periodontal diseases, 22 oral cancer, 23 periapical lesions, 24 radiolucent lesions, 25 cystic lesions, 26 cephalometric analysis, 27 and debonding of computer-aided design/computer-aided manufacturing (CAD/CAM) crowns. 28 Future systems are expected to be increasingly autonomous, going beyond recommending possible clinical actions to perform certain tasks independently, such as patient trial and screening references. 29,30 Many experts have stated their views on the future of radiology following the emergence of AI, 31,32 and white papers published by radiological societies and promoting views on this.…”

Section: Introductionmentioning

confidence: 99%

Attitudes and perceptions of dental students towards artificial intelligence

Yüzbaşıoğlu

2020

Journal of Dental Education

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IntroductionArtificial Intelligence (AI) is a burning topic and use of AI in our day‐to‐day life has increased exponentially. The purpose of this study was to evaluate the attitudes and perceptions of Turkish dental students towards AI and to provide information about their opinions to use of AI in dentistry.MethodsA 22‐question survey was distributed through Google Forms to all dental students at 9 Turkish dental schools in March 2019. It consisted of various sections aiming to evaluate the dental students’ knowledge and attitudes toward AI and possible applications in dentistry. Respondents’ anonymity was ensured.ResultsA total of 1103 students (650 female, 453 male; mean age was 21.36 ± 1.93 years) responded to the questionnaire for a 21.69% response rate. Of these, about 48.40% had basic knowledge about AI technologies and 10.6% stated that they had no information sources about AI. 85.70% agreed that dentistry would be revolutionized by AI but 28.60% did not agree that AI could replace themselves in the near future. In addition, 74.60% and 79.80% of agreed on topics about AI to be included in undergraduate and postgraduate dental education, respectively.ConclusionsAlthough participants have insufficient knowledge of AI, they are willing to improve their knowledge in this field. Participants shared an optimistic views and thought that AI will have a positive impact on dental future practice. Follow‐up surveys and multinational studies should be conducted to further explore these issues.

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Predicting the Debonding of CAD/CAM Composite Resin Crowns with AI

Cited by 86 publications

References 23 publications

Application of Artificial Intelligence in Dentistry

Application of Artificial Intelligence in Dentistry

Identification of dental implants using deep learning—pilot study

Attitudes and perceptions of dental students towards artificial intelligence

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