Deep Neural Networks for Dental Implant System Classification

Sukegawa, Shintaro; Yoshii, Kazumasa; Hara, Takeshi; Yamashita, Katsusuke; Nakano, Keisuke; Yamamoto, Naokatsu; Nagatsuka, Hitoshi; Furuki, Yoshihiko

doi:10.3390/biom10070984

Cited by 120 publications

(118 citation statements)

References 32 publications

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“…All CNNs were used for transfer learning with fine-tuning because a pretrained model reduces the training time and the number of images required to create a suitable classifier [ 37 ]. The DL process was performed with the Python package PyTorch.…”

Section: Methodsmentioning

confidence: 99%

Deep Learning for Osteoporosis Classification Using Hip Radiographs and Patient Clinical Covariates

Yamamoto

Sukegawa

Kitamura³

et al. 2020

Biomolecules

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This study considers the use of deep learning to diagnose osteoporosis from hip radiographs, and whether adding clinical data improves diagnostic performance over the image mode alone. For objective labeling, we collected a dataset containing 1131 images from patients who underwent both skeletal bone mineral density measurement and hip radiography at a single general hospital between 2014 and 2019. Osteoporosis was assessed from the hip radiographs using five convolutional neural network (CNN) models. We also investigated ensemble models with clinical covariates added to each CNN. The accuracy, precision, recall, specificity, negative predictive value (npv), F1 score, and area under the curve (AUC) score were calculated for each network. In the evaluation of the five CNN models using only hip radiographs, GoogleNet and EfficientNet b3 exhibited the best accuracy, precision, and specificity. Among the five ensemble models, EfficientNet b3 exhibited the best accuracy, recall, npv, F1 score, and AUC score when patient variables were included. The CNN models diagnosed osteoporosis from hip radiographs with high accuracy, and their performance improved further with the addition of clinical covariates from patient records.

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Section: Methodsmentioning

confidence: 99%

Deep Learning for Osteoporosis Classification Using Hip Radiographs and Patient Clinical Covariates

Yamamoto

Sukegawa

Kitamura³

et al. 2020

Biomolecules

Self Cite

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“…17 Additionally, ML models have been created that are able to classify up to 11 different implant systems from cropped images of OPGs and periapical (PA) radiographs, achieving accuracies of over 90%. [18][19][20] Other CNN-based ML models have classified tooth types from cone beam computed tomography (CBCT) and digitally scanned plaster models. 21,22 Caries Current ML research is directed at detecting primary carious lesions on dental images.…”

Section: Odontogrammentioning

confidence: 99%

Promises and perils of artificial intelligence in dentistry

Pethani

2021

Australian Dental Journal

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Artificial intelligence (AI) is a subdiscipline of computer science that has made substantial progress in medicine and there is a growing body of AI research in dentistry. Dentists should have an understanding of the foundational concepts and the ability to critically evaluate dental research in AI. Machine learning (ML) is a subfield of AI that most dental AI research is dedicated to. The most prolific area of ML research is automated interpretation of dental imaging. Other areas include providing treatment recommendations, predicting future disease and treatment outcomes. The research impact is limited by small datasets that do not harness the positive correlation between very large datasets and ML performance. There is also a need to standardize research methodologies and utilize performance metrics that are appropriate for the clinical context. In addition to research challenges, this article discusses the ethical, legal and logistical considerations associated with implementation in clinical practice. This includes explainable AI, model bias, data privacy and security. The future implications of AI in dentistry involve a promise for a novel form of practicing dentistry however, the effect of AI on patient outcomes is yet to be determined.

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“…Patients may also have difficulty in visiting the hospital for various reasons, such as deterioration of the patient’s general condition, transfer, migration to other country, and closure of dental clinics. Therefore, the identity of implant manufacturers should be made visible in XP images or a global implant sharing system should be developed to facilitate the removal of implanted structures in unknown systems ( 28 ).…”

Section: Resultsmentioning

confidence: 99%

Clinical retrospective study of dental implant removal: do patients who require implant removal desire implant prosthesis again?

et al. 2020

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Background This study investigated the causes of dental implant removal due to complications, and examined whether patients who had dental implant removal desired re-implant prosthesis treatments. Material and Methods A retrospective case–control study was conducted on patients who had their dental implants removed. We investigated whether the removed dental implant was replaced with other implant prostheses. Age, sex, diabetes, smoking, implant site distribution, reason for implant removal, and blade and root-form implants were categorized as predictive variables. The outcome variable was desire for re-implantation or use of other prosthetic methods after implant removal. A logistic regression model was created to identify patient factors that could predict the re-implantation of dental prostheses after implant removal. Results A total of 215 dental implants were removed from 143 patients. The most common reason for implant removal was peri-implantitis that was identified in 165 implants. After implant removal, re-implantation was performed in 98 implants (45.6%). Bivariate analyses showed that age, diabetes, implant type, and reason for implant removal were associated with the desire for re-implanted prostheses. The multiple regression model revealed that age, implant type, and reason for implant removal were associated with an increased desire for re-implant prostheses after implant removal. Conclusions Re-implantation of prostheses after the removal of dental implants was desired by patients who were younger, had implants placed in the root form, and had implants removed due to prosthetic-related complications. Key words: Dental implant removal, complications, implant prosthesis, retrospective study, re-implantation.

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Deep Neural Networks for Dental Implant System Classification

Cited by 120 publications

References 32 publications

Deep Learning for Osteoporosis Classification Using Hip Radiographs and Patient Clinical Covariates

Deep Learning for Osteoporosis Classification Using Hip Radiographs and Patient Clinical Covariates

Promises and perils of artificial intelligence in dentistry

Clinical retrospective study of dental implant removal: do patients who require implant removal desire implant prosthesis again?

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