4.6 Review

Application and Performance of Artificial Intelligence Technology in Detection, Diagnosis and Prediction of Dental Caries (DC)-A Systematic Review

Related references

Note: Only part of the references are listed.
Article Computer Science, Artificial Intelligence

CariesNet: a deep learning approach for segmentation of multi-stage caries lesion from oral panoramic X-ray image

Haihua Zhu et al.

Summary: Dental caries is a common global health issue, and timely and effective treatment is crucial to reduce pain. This study proposes a deep learning architecture called CariesNet to analyze different degrees of caries from panoramic radiographs, achieving high accuracy and segmentation performance.

NEURAL COMPUTING & APPLICATIONS (2023)

Review Biology

Review and classification of AI-enabled COVID-19 CT imaging models based on computer vision tasks

Haseeb Hassan et al.

Summary: This article provides a systematic overview of AI and computer vision strategies for diagnosing COVID-19 using CT medical images. The author found that previous reviews did not classify COVID-19 literature based on computer vision tasks. The article focuses on CT-based diagnostic methods and collects 114 relevant studies.

COMPUTERS IN BIOLOGY AND MEDICINE (2022)

Article Dentistry, Oral Surgery & Medicine

Development and evaluation of deep learning for screening dental caries from oral photographs

Xuan Zhang et al.

Summary: This study developed and evaluated a deep learning system based on convolutional neural network (ConvNet) to detect dental caries from oral photographs. The system exhibited high classification and localization accuracy, making it promising for preliminary screening of dental caries in large populations.

ORAL DISEASES (2022)

Article Dentistry, Oral Surgery & Medicine

Global Burden and Inequality of Dental Caries, 1990 to 2019

P. Y. F. Wen et al.

Summary: Dental caries remains a global public health challenge, with lower prevalence in permanent teeth in developed countries and population growth being a key driver of changes in the number of caries cases.

JOURNAL OF DENTAL RESEARCH (2022)

Article Dentistry, Oral Surgery & Medicine

Deep-learning approach for caries detection and segmentation on dental bitewing radiographs

Ibrahim Sevki Bayrakdar et al.

Summary: The study demonstrates the potential of AI models based on CNN algorithm in detecting and segmenting dental caries in bitewing radiographs, showing superiority of AI models over assistant specialists on external datasets.

ORAL RADIOLOGY (2022)

Review Dentistry, Oral Surgery & Medicine

Artificial intelligence applications in restorative dentistry: A systematic review

Marta Revilla-Leon et al.

Summary: This systematic review evaluates the ability of artificial intelligence (AI) models in restorative dentistry applications. The analysis of 34 articles reveals that AI models have the potential to assist in the diagnosis of dental caries and vertical tooth fracture, detect tooth preparation margins, and predict restoration failure. However, further research and evaluation are needed for the dental applications of AI models in restorative dentistry.

JOURNAL OF PROSTHETIC DENTISTRY (2022)

Article Medicine, General & Internal

Deep Learning Applications in Computed Tomography Images for Pulmonary Nodule Detection and Diagnosis: A Review

Rui Li et al.

Summary: This review focuses on the segmentation and classification parts of lung cancer diagnosis, discussing the methods of lung nodule segmentation using different network architectures and organizing the essential datasets and evaluation metrics for lung nodule detection and diagnosis.

DIAGNOSTICS (2022)

Article Dentistry, Oral Surgery & Medicine

Diagnosis of interproximal caries lesions with deep convolutional neural network in digital bitewing radiographs

Yusuf Bayraktar et al.

Summary: The study demonstrates that deep convolutional neural networks show good performance in diagnosing caries lesions in digital bitewing radiographs, providing assistance for accurate diagnosis and treatment.

CLINICAL ORAL INVESTIGATIONS (2022)

Article Dentistry, Oral Surgery & Medicine

Caries Detection on Intraoral Images Using Artificial Intelligence

J. Kuehnisch et al.

Summary: This study aimed to develop a deep learning approach for caries detection and compare it with expert standards, showing that artificial intelligence achieved over 90% agreement in caries detection on intraoral images. Although highly accurate, further improvements are still necessary for the current approach.

JOURNAL OF DENTAL RESEARCH (2022)

Article Dentistry, Oral Surgery & Medicine

The ADEPT study: a comparative study of dentists' ability to detect enamel-only proximal caries in bitewing radiographs with and without the use of AssistDent artificial intelligence software

Hugh Devlin et al.

Summary: The study found that the use of AssistDent AI software significantly improves dentists' ability to detect enamel-only proximal caries, with a higher sensitivity in identifying potential carious lesions, despite an increase in misdiagnosis on healthy surfaces.

BRITISH DENTAL JOURNAL (2021)

Article Dentistry, Oral Surgery & Medicine

Cost-effectiveness of Artificial Intelligence for Proximal Caries Detection

F. Schwendicke et al.

Summary: The study compared the cost-effectiveness of AI-assisted proximal caries detection in dental diagnostics, showing that AI was more accurate and sensitive than dentists in detecting caries. In the majority of cases, AI was found to be more cost-effective and provided better results.

JOURNAL OF DENTAL RESEARCH (2021)

Article Dentistry, Oral Surgery & Medicine

Artificial intelligence for caries detection: Randomized trial

Sarah Mertens et al.

Summary: The study found that artificial intelligence software can improve dentists' diagnostic accuracy for proximal caries, especially in increasing sensitivity for detecting enamel lesions, but may also increase decisions for invasive treatments. Differences in the effects of AI on individual dentists should be further explored, and guidance should be provided on therapy choices when detecting caries lesions with AI support.

JOURNAL OF DENTISTRY (2021)

Review Chemistry, Analytical

The Use and Performance of Artificial Intelligence in Prosthodontics: A Systematic Review

Selina A. Bernauer et al.

Summary: This systematic review explores the application of AI in prosthodontics, focusing on the training and use of AI systems. Despite the relatively low number of studies included, the findings demonstrate the potential of AI technology in personalized dental treatment for patients.

SENSORS (2021)

Article Engineering, Biomedical

Dental disease detection on periapical radiographs based on deep convolutional neural networks

Hu Chen et al.

Summary: The study aimed to develop an auxiliary diagnosis system for dental periapical radiographs based on deep CNNs. Results showed that the CNNs prefer to detect lesions with severe levels, and it is recommended to train the CNNs with customized strategy for each disease to improve performance.

INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY (2021)

Article Genetics & Heredity

A New Model for Caries Risk Prediction in Teenagers Using a Machine Learning Algorithm Based on Environmental and Genetic Factors

Liangyue Pang et al.

Summary: A new caries risk prediction model for teenagers was developed based on environmental and genetic factors using a machine learning algorithm. The model showed high discrimination ability in identifying individuals at high and very high caries risk, but underestimated risks for those at low and very low risk levels. This model has the potential to be a powerful tool at the community level for identifying individuals at high risk of caries.

FRONTIERS IN GENETICS (2021)

Article Health Care Sciences & Services

Automated caries detection with smartphone color photography using machine learning

Duc Long Duong et al.

Summary: This study introduces a computational algorithm for automated recognition of carious lesions on tooth occlusal surfaces according to ICDAS. Using smartphone images of 620 teeth, a two-step detection scheme was proposed with SVM, showing promising potential for clinical diagnostics with reasonable accuracy and minimal cost.

HEALTH INFORMATICS JOURNAL (2021)

Article Dentistry, Oral Surgery & Medicine

Detecting white spot lesions on dental photography using deep learning: A pilot study

Haitham Askar et al.

Summary: Deep learning proves to have satisfactory accuracy in detecting white spot lesions in dental photographs, especially fluorotic lesions. Models trained to detect different types of lesions showed similar performance, with lower sensitivity. False positive detections were mainly attributed to light reflections.

JOURNAL OF DENTISTRY (2021)

Article Genetics & Heredity

Prediction of Early Childhood Caries Based on Single Nucleotide Polymorphisms Using Neural Networks

Katarzyna Zaorska et al.

Summary: The study found that a neural network prediction model could increase accuracy in predicting early childhood caries, potentially serving as a valuable tool for screening and early prevention of high-risk patients.

GENES (2021)

Article Oncology

Artificial intelligence in digital cariology: a new tool for the diagnosis of deep caries and pulpitis using convolutional neural networks

Liwen Zheng et al.

Summary: Through studying 844 radiographs, it was found that the CNN of ResNet18 demonstrated the best performance for diagnosing deep caries and pulpitis. Further integration with clinical parameters resulted in significantly enhanced diagnostic performance.

ANNALS OF TRANSLATIONAL MEDICINE (2021)

Article Radiology, Nuclear Medicine & Medical Imaging

Prediction of Radiation-Related Dental Caries Through PyRadiomics Features and Artificial Neural Network on Panoramic Radiography

Vanessa De Araujo Faria et al.

Summary: This study introduces a reliable method using artificial intelligence neural network and PyRadiomics features to predict and detect radiation-related caries (RRC) in head and neck cancer patients under radiotherapy, achieving a sensitivity of 98.8% for RRC detection and an accuracy of 99.2% for RRC prediction.

JOURNAL OF DIGITAL IMAGING (2021)

Article Chemistry, Analytical

Caries and Restoration Detection Using Bitewing Film Based on Transfer Learning with CNNs

Yi-Cheng Mao et al.

Summary: Caries, a dental disease caused by bacterial infection, can be easier to treat and prevent from spreading if detected early. Utilizing AI imaging research and technical methods can assist dentists in accurate markings and treatment planning, shortening professionals' judgment time. The proposed AlexNet model in this study shows promising accuracy in caries and restoration judgments, suggesting the potential for developing an automatic judgment method for bitewing films.

SENSORS (2021)

Article Chemistry, Analytical

Classification of Approximal Caries in Bitewing Radiographs Using Convolutional Neural Networks

Maira Moran et al.

Summary: This study proposes a method that combines image processing techniques and convolutional neural networks to identify approximal dental caries in bitewing radiographic images and classify them according to lesion severity. Using 112 radiographs, individual tooth images were extracted, labeled, and used to train CNN models. Evaluation was performed using different learning rates and architectures, with the Inception model achieving the best accuracy of 73.3% at a learning rate of 0.001. The results suggest that the proposed method could be useful in assisting dentists in evaluating bitewing images and defining lesion severity for appropriate treatments.

SENSORS (2021)

Article Multidisciplinary Sciences

Deep learning for early dental caries detection in bitewing radiographs

Shinae Lee et al.

Summary: The study developed a CNN model using U-Net for caries detection on bitewing radiographs, and demonstrated that the model can help clinicians diagnose caries more accurately, especially in cases of initial and moderate caries, as shown by the improved diagnostic performance of three dentists using the model's results.

SCIENTIFIC REPORTS (2021)

Article Multidisciplinary Sciences

Classification of caries in third molars on panoramic radiographs using deep learning

Shankeeth Vinayahalingam et al.

Summary: The study assessed the classification accuracy of dental caries on panoramic radiographs using deep-learning algorithms, achieving high accuracy for the classification of carious lesions in third molars. This method could benefit the future development of a deep-learning based automated third molar removal assessment.

SCIENTIFIC REPORTS (2021)

Article Multidisciplinary Sciences

Machine learning to predict distal caries in mandibular second molars associated with impacted third molars

Sung-Hwi Hur et al.

Summary: This study aimed to develop and validate five machine learning models for predicting distal caries on adjacent mandibular second molars, and to determine the relative importance of predictive variables. The performance of these models was significantly better than single predictors, with area under the receiver operating characteristic curve ranging from 0.88 to 0.89. Six key features were identified as relevant predictors, offering valuable insights for clinical decision making.

SCIENTIFIC REPORTS (2021)

Article Environmental Sciences

Prediction Models of Early Childhood Caries Based on Machine Learning Algorithms

You-Hyun Park et al.

Summary: The study compared the performance of machine learning-based and traditional regression models for predicting early childhood caries, finding both to be equally effective in predicting and identifying high-risk groups, although utilizing new methods like deep learning may enhance model performance.

INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH (2021)

Article Immunology

Machine Learning Approach Identified Multi-Platform Factors for Caries Prediction in Child-Mother Dyads

Tong Tong Wu et al.

Summary: Untreated tooth decays affect approximately one third of the world's population, particularly children, and the disease progression is influenced by multiple factors. Utilizing machine learning and bacterial sequencing can potentially predict tooth decay, highlighting the significance of fungal and environmental factors in preventive and diagnostic interventions.

FRONTIERS IN CELLULAR AND INFECTION MICROBIOLOGY (2021)

Review Dentistry, Oral Surgery & Medicine

Applications of artificial intelligence and machine learning in orthodontics: a scoping review

Yashodhan M. Bichu et al.

Summary: This scoping review provides an overview of the current status and limitations of artificial intelligence and machine learning in orthodontics. The study found an exponential increase in research involving AI/ML applications, with a focus on diagnosis and treatment planning, automated anatomic landmark detection and analyses, and growth and development assessment in the past decade.

PROGRESS IN ORTHODONTICS (2021)

Article Medicine, General & Internal

Deep Learning for Caries Detection and Classification

Luya Lian et al.

Summary: Deep learning methods were used to detect and classify caries lesions on dental panoramic radiographs, showing similar performance to expert dentists in terms of accuracy, recall, NPV, and other metrics, suggesting potential clinical applications.

DIAGNOSTICS (2021)

Article Medicine, General & Internal

Proof-of-Concept Study on an Automatic Computational System in Detecting and Classifying Occlusal Caries Lesions from Smartphone Color Images of Unrestored Extracted Teeth

Duc Long Duong et al.

Summary: This study aimed to develop a two-stage computational system for early detection of occlusal caries from smartphone images, achieving promising results and confirming the feasibility of using artificial intelligence algorithms in caries detection. Furthermore, improvements in in vitro and in vivo modeling, as well as the development of a system for accurate intra-oral imaging, are needed to enhance the proposed system's performance.

DIAGNOSTICS (2021)

Article Dentistry, Oral Surgery & Medicine

Using a Machine Learning Algorithm to Predict the Likelihood of Presence of Dental Caries among Children Aged 2 to 7

Francisco Ramos-Gomez et al.

Summary: This study explores the potential of using a machine learning algorithm with parental oral health surveys to screen for dental caries in children. Results showed that factors like parent's age, unmet needs, and previous oral health problems were strong predictors for active caries and caries experience in children. It concludes that screening through parental surveys has the potential for identifying dental caries in children.

DENTISTRY JOURNAL (2021)

Review Dentistry, Oral Surgery & Medicine

The use and performance of artificial intelligence applications in dental and maxillofacial radiology: A systematic review

Kuofeng Hung et al.

DENTOMAXILLOFACIAL RADIOLOGY (2020)

Article Dentistry, Oral Surgery & Medicine

Deep learning for caries lesion detection in near-infrared light transillumination images: A pilot study

Falk Schwendicke et al.

JOURNAL OF DENTISTRY (2020)

Article Multidisciplinary Sciences

Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer

Xueyi Zheng et al.

NATURE COMMUNICATIONS (2020)

Article Multidisciplinary Sciences

Deep Learning Hybrid Method to Automatically Diagnose Periodontal Bone Loss and Stage Periodontitis

Hyuk-Joon Chang et al.

SCIENTIFIC REPORTS (2020)

Article Dentistry, Oral Surgery & Medicine

Automatic diagnosis for cysts and tumors of both jaws on panoramic radiographs using a deep convolution neural network

Odeuk Kwon et al.

DENTOMAXILLOFACIAL RADIOLOGY (2020)

Article Medicine, General & Internal

Deep Learning for Automated Detection of Cyst and Tumors of the Jaw in Panoramic Radiographs

Hyunwoo Yang et al.

JOURNAL OF CLINICAL MEDICINE (2020)

Article Dentistry, Oral Surgery & Medicine

Thermal Imaging of Root Caries In Vivo

V. Yang et al.

JOURNAL OF DENTAL RESEARCH (2020)

Article Dentistry, Oral Surgery & Medicine

Detecting caries lesions of different radiographic extension on bitewings using deep learning

Anselmo Garcia Cantu et al.

JOURNAL OF DENTISTRY (2020)

Article Medical Informatics

Dental caries diagnosis in digital radiographs using back-propagation neural network

V. Geetha et al.

HEALTH INFORMATION SCIENCE AND SYSTEMS (2020)

Review Dentistry, Oral Surgery & Medicine

Early childhood caries epidemiology, aetiology, risk assessment, societal burden, management, education, and policy: Global perspective

Norman Tinanoff et al.

INTERNATIONAL JOURNAL OF PAEDIATRIC DENTISTRY (2019)

Article Dentistry, Oral Surgery & Medicine

Application of machine learning for diagnostic prediction of root caries

Man Hung et al.

GERODONTOLOGY (2019)

Editorial Material Anesthesiology

Use of the GRADE approach in systematic reviews and guidelines

Anders Granholm et al.

BRITISH JOURNAL OF ANAESTHESIA (2019)

Article Dentistry, Oral Surgery & Medicine

Caries Detection with Near-Infrared Transillumination Using Deep Learning

F. Casalegno et al.

JOURNAL OF DENTAL RESEARCH (2019)

Article Computer Science, Information Systems

Boosting Proximal Dental Caries Detection via Combination of Variational Methods and Convolutional Neural Network

Joonhyang Choi et al.

JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY (2018)

Article Dentistry, Oral Surgery & Medicine

Global-, Regional-, and Country-Level Economic Impacts of Dental Diseases in 2015

A. J. Righolt et al.

JOURNAL OF DENTAL RESEARCH (2018)

Article Dentistry, Oral Surgery & Medicine

Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm

Jae-Hong Lee et al.

JOURNAL OF DENTISTRY (2018)

Article Multidisciplinary Sciences

Dermatologist-level classification of skin cancer with deep neural networks

Andre Esteva et al.

NATURE (2017)

Article Dentistry, Oral Surgery & Medicine

Radiographic diagnosis of proximal caries-influence of experience and gender of the dental staff

Margrit-Ann Geibel et al.

CLINICAL ORAL INVESTIGATIONS (2017)

Article Medicine, General & Internal

QUADAS-2: A Revised Tool for the Quality Assessment of Diagnostic Accuracy Studies

Penny F. Whiting et al.

ANNALS OF INTERNAL MEDICINE (2011)

Article Dentistry, Oral Surgery & Medicine

The Paradox of Better Subjective Oral Health in Older Age

G. D. Slade et al.

JOURNAL OF DENTAL RESEARCH (2011)

Article Dentistry, Oral Surgery & Medicine

Assessment of the accuracy of visual examination, bite-wing radiographs and DIAGNOdent r on the diagnosis of occlusal caries

A. M. Costa et al.

EUROPEAN ARCHIVES OF PAEDIATRIC DENTISTRY (2007)

Article Dentistry, Oral Surgery & Medicine

Diagnosis of approximal caries: Bite-wing radiology versus the Ultrasound Caries Detector. An in vitro study

S Matalon et al.

ORAL SURGERY ORAL MEDICINE ORAL PATHOLOGY ORAL RADIOLOGY AND ENDODONTOLOGY (2003)

Article Dentistry, Oral Surgery & Medicine

A systematic review of the performance of methods for identifying carious lesions

JD Bader et al.

JOURNAL OF PUBLIC HEALTH DENTISTRY (2002)

Article Dentistry, Oral Surgery & Medicine

The real performance of bitewing radiography and fiber-optic transillumination in approximal caries diagnosis

J Vaarkamp et al.

JOURNAL OF DENTAL RESEARCH (2000)