An artificial intelligence system using machine-learning for automatic detection and classification of dental restorations in panoramic radiography

Abdalla‐Aslan, Ragda; Yeshua, Talia; Kabla, Daniel; Leichter, Isaac; Nadler, Chen

doi:10.1016/j.oooo.2020.05.012

Cited by 91 publications

(86 citation statements)

References 24 publications

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“…Several ML models have been created and applied to OPGs for automating detection of teeth and classifying restorations. [15][16][17] The models perform relatively poorly when OPGs contain impacted teeth, dental artefacts such as dental implants and areas of higher contrast such as the presence of overlap, enamel, external oblique ridge and pulp stones. ML models can now identify individual teeth and classify multiple restorations on the same tooth such as dental implant and crown with an accuracy of 93.6%.…”

Section: Odontogrammentioning

confidence: 99%

“…ML models can now identify individual teeth and classify multiple restorations on the same tooth such as dental implant and crown with an accuracy of 93.6%. 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.…”

Section: Odontogrammentioning

confidence: 99%

“…Orthopantomograms (OPG) present a challenge for automated charting due to the high number of superimposed anatomical structures. Several ML models have been created and applied to OPGs for automating detection of teeth and classifying restorations 15–17 . The models perform relatively poorly when OPGs contain impacted teeth, dental artefacts such as dental implants and areas of higher contrast such as the presence of overlap, enamel, external oblique ridge and pulp stones.…”

Section: Machine Learningmentioning

confidence: 99%

“…The models perform relatively poorly when OPGs contain impacted teeth, dental artefacts such as dental implants and areas of higher contrast such as the presence of overlap, enamel, external oblique ridge and pulp stones. ML models can now identify individual teeth and classify multiple restorations on the same tooth such as dental implant and crown with an accuracy of 93.6% 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% 18–20 .…”

Section: Machine Learningmentioning

confidence: 99%

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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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Section: Odontogrammentioning

confidence: 99%

Section: Odontogrammentioning

confidence: 99%

Section: Machine Learningmentioning

confidence: 99%

Section: Machine Learningmentioning

confidence: 99%

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Promises and perils of artificial intelligence in dentistry

Pethani

2021

Australian Dental Journal

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“…As AlphaGo was developed by Google (DeepMind Technologies Limited) in 2014, the interest of the general public in artificial intelligence increased rapidly. Artificial intelligence is currently affecting our lives and is widely used in industries and surrounding environments and in medical fields for diagnosis, treatment, and dentistry [1][2][3] . Moreover, platforms with various artificial intelligence libraries such as TensorFlow, Keras, and Pytorch, etc.…”

Section: Introductionmentioning

confidence: 99%

Feasibility Study of Google’s Teachable Machine in Diagnosis of Tooth-Marked Tongue

Jeong¹

2020

J Dent Hyg Sci

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Automatic semantic segmentation for dental restorations in panoramic radiography images using U‐Net model

Öztekin

KATAR

Sadak

et al. 2022

Int J Imaging Syst Tech

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The automated segmentation of dental restorations is a critical step in diagnosing dental problems and suggesting the best treatment. Some restorations may be missed during a dental examination, depending on the number of patients, the dentist's experience, and fatigue. Automatic detection of dental restorations based on deep learning has the potential to provide a quick radiological assessment based on the patient's treatment history and pre-diagnosis. This study presents a deep learning-based method for automatic detection and classification of amalgam and composite fillings on panoramic images. A total of 250 anonymized panoramic images with amalgam and composite fillings with a resolution of 2048 Â 1024 px were used. In this study, U-Net models with various backbones were employed. The ResNext50 model has achieved the highest pixel accuracy and intersection over union (IoU) performance based on the evaluation of various ResNet and ResNext backbones. The mean IoU value obtained by the model on the test images is 0.767 while the Pixel Accuracy of 99.81% was achieved. Our proposed method demonstrated superior performance compared to similarly conducted studies in the literature. The proposed method can potentially be employed in clinical settings to detect dental restorations automatically. The classification and detection of dental restorations with this model can aid dentistry education at higher institutions as an education tool and make the reporting easier for the dentist.

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An artificial intelligence system using machine-learning for automatic detection and classification of dental restorations in panoramic radiography

Cited by 91 publications

References 24 publications

Promises and perils of artificial intelligence in dentistry

Promises and perils of artificial intelligence in dentistry

Feasibility Study of Google’s Teachable Machine in Diagnosis of Tooth-Marked Tongue

Automatic semantic segmentation for dental restorations in panoramic radiography images using U‐Net model

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