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Article
Computer Science, Artificial Intelligence
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)
Article
Multidisciplinary Sciences
Adnan Qayyum et al.
Summary: This article proposes an efficient self-training method for caries detection and segmentation, using a small set of labelled images to train the teacher model and a large collection of unlabelled images to train the student model. By using centroid cropped images of the caries region and different augmentation techniques for self-supervised training, computational and performance gains are achieved compared to fully supervised learning and standard self-supervised learning methods. Evaluation results show that our proposed self-supervised learning strategy improves average pixel accuracy and mean intersection over union by approximately 6% and 3%, respectively, compared to standard self-supervised learning.
SCIENTIFIC REPORTS
(2023)
Article
Medicine, General & Internal
Faruk Oztekin et al.
Summary: Dental caries is a common dental health issue that can cause pain and infections, reducing quality of life. Applying machine learning models for caries detection can lead to early treatment, but lack of explainability may hinder their acceptance. In this study, an explainable deep learning model for detecting dental caries was developed and evaluated. The ResNet-50 model showed slightly better performance compared to EfficientNet-B0 and DenseNet-121, achieving an accuracy of 92.00% and a sensitivity of 87.33%. The heat maps provided by the model helped explain the classification results, enabling dentists to validate and reduce misclassification.
Article
Engineering, Biomedical
Yue Xu et al.
Summary: A stimuli-responsive multidrug delivery system has been developed in this study, which can prevent tooth decay and promote enamel restoration. The system can identify cariogenic conditions, intelligently release drugs, and restore the microarchitecture and mechanical properties of demineralized teeth.
BIOACTIVE MATERIALS
(2023)
Article
Dentistry, Oral Surgery & Medicine
Wannakamon Panyarak et al.
Summary: The study aimed to evaluate the potential of deep learning models for categorization of dental caries in bitewing radiographs based on the ICCMSTM radiographic scoring system. The study divided 2758 annotated bitewing radiographs into 3 experiments and assessed the performance of ResNet-18, -50, -101, and -152 models. The results showed that the ResNet models had average performances in classifying dental caries according to ICCMSTM-RSS, but underperformed in complicated classification tasks.
ORAL SURGERY ORAL MEDICINE ORAL PATHOLOGY ORAL RADIOLOGY
(2023)
Review
Biochemistry & Molecular Biology
Satish Vishwanathaiah et al.
Summary: In the era of global epidemic, oral problems have a significant impact on a large population of children. Early diagnosis, prevention, and treatment of these disorders are crucial for children's optimal health. Artificial intelligence (AI) has made tremendous progress in recent years and infiltrated even traditionally human-specialist domains. AI models are frequently used in pediatric dentistry for accurate diagnosis, assisting clinicians and dentists in decision making, developing preventive strategies, and establishing treatment plans.
Article
Health Care Sciences & Services
Jelena Roganovic et al.
Summary: The introduction of AI-based dental applications in clinical practice can improve diagnostic accuracy and reform dental care. However, the readiness of dentists and the health system to adopt AI is crucial for its implementation. A survey among experienced dentists and final-year undergraduate students revealed a lack of knowledge about AI and skepticism towards its use. Reasons for this included a lack of knowledge about the technology and fear of being replaced by AI, as well as a lack of regulatory policy. Female dentists were more concerned about ethical issues related to AI implementation. These results highlight the need for an ethical debate and regulatory policies for AI in dental practice.
Review
Biodiversity Conservation
Vasco Veiga Branco et al.
Summary: The concepts and methodologies of machine learning are increasingly used for creating semi-autonomous programmes that can adapt to various problems and decision-making scenarios. This systematic review summarizes the use of machine learning methods in studying species threats and conservation measures, and identifies the emerging trends. Maximum entropy, Bayesian models, ensemble methods, and other algorithms have gained popularity for various conservation problems due to their relevance, ease of implementation, and availability in software packages.
BIOLOGICAL CONSERVATION
(2023)
Article
Dentistry, Oral Surgery & Medicine
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.
Review
Dentistry, Oral Surgery & Medicine
Francisco Carrillo-Perez et al.
Summary: This comprehensive review examines the use of artificial intelligence and machine learning in dentistry, focusing on deep learning, fuzzy logic, and other techniques applied to disease identification, image segmentation, image correction, and color analysis. The study highlights the potential for high-performance decision support systems and personalized treatments in digital dentistry, with an emphasis on improving the accuracy of dental restorations in esthetic dentistry through advanced modeling techniques.
JOURNAL OF ESTHETIC AND RESTORATIVE DENTISTRY
(2022)
Article
Dentistry, Oral Surgery & Medicine
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.
Article
Dentistry, Oral Surgery & Medicine
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
Vincent Majanga et al.
TheScientificWorldJOURNAL
(2022)
Article
Dentistry, Oral Surgery & Medicine
Ravi Kumar Gudipaneni et al.
Summary: This study evaluated the diagnostic potential of different caries assessment tools for estimating the caries prevalence rate of the first permanent molar in Saudi male children aged 7-9 years. The results showed that enamel caries lesions were found in more than half of the children. The CAST index was recommended as it detects the complete spectrum of caries. The ICDAS II codes 1-6 and CAST codes 3-7 projected similar caries prevalence rates in the first permanent molars.
Article
Environmental Sciences
Agata Ossowska et al.
Summary: Artificial intelligence and neural networks are becoming increasingly important in medicine and dentistry, offering improved efficiency, accuracy, and time-saving during diagnosis and treatment planning. Further research and development are needed to fully integrate these technologies into daily dental practice.
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
(2022)
Article
Chemistry, Multidisciplinary
In-Ae Kang et al.
Summary: Dental caries is an infectious disease that deteriorates tooth structure, leading to the formation of cavities. Research has been conducted to detect caries early due to pain and treatment costs. However, traditional research faces limitations in terms of funds and time. In recent years, artificial intelligence has been used to develop models that can predict the risk of dental caries. Random forest, as a machine learning algorithm, has shown the best performance in terms of accuracy, F1-score, precision, and recall.
APPLIED SCIENCES-BASEL
(2022)
Review
Medicine, General & Internal
Sanjeev B. Khanagar et al.
Summary: AI technology has been widely used in the diagnosis of oral diseases, demonstrating excellent performance in enhancing diagnostic accuracy and treatment quality, and identifying high-risk patients.
Article
Medicine, General & Internal
Xiujiao Lin et al.
Summary: This study evaluated the performance of convolutional neural networks (CNNs) trained with small datasets in detecting proximal caries on periapical radiographs. The results showed that CNN trained with the edge extraction strategy performed the best in detecting proximal caries.
Review
Medicine, General & Internal
Shankargouda Patil et al.
Summary: AI applications in the diagnosis of oral diseases, through clinical data and diagnostic images, can predict disease occurrence and improve diagnostic efficiency. Despite being in the research phase, the coming decade will witness significant changes and advancements in diagnosis and healthcare driven by AI.
Article
Health Care Sciences & Services
Lena Petersson et al.
Summary: This study explores the challenges faced by healthcare leaders in a regional Swedish healthcare setting regarding the implementation of AI in healthcare. The findings reveal challenges related to the conditions both inside and outside the healthcare system, the capacity for strategic change management, and the transformation of healthcare professions and practice.
BMC HEALTH SERVICES RESEARCH
(2022)
Review
Dentistry, Oral Surgery & Medicine
Hossein Mohammad-Rahimi et al.
Summary: This study aims to systematically review deep learning studies on caries detection. The results show promising accuracy of deep learning models in caries detection, although the quality of the studies and reporting is currently low.
JOURNAL OF DENTISTRY
(2022)
Article
Syed Sarosh Mahdi et al.
International Journal of Information Management Data Insights
(2022)
Review
Health Care Sciences & Services
Samah AbuSalim et al.
Summary: Dental informatics is a growing field in the healthcare industry, and the use of deep learning techniques to address dental informatics problems is of great importance. Current research focuses on building comprehensive and meaningful interpretable structures from complex data, and highlights the need for better technique development and new perspectives in this exciting new development.
Review
Medicine, General & Internal
Paridhi Agrawal et al.
Summary: Artificial intelligence (AI) has gained significant presence and importance in various sectors, including dentistry. In endodontics, AI models such as convolutional neural networks and artificial neural networks have been applied for complex predictions and decision-making, showing potential in tasks such as studying root canal anatomy and predicting treatment outcomes. However, further certification of cost-effectiveness, dependability, and applicability is necessary before integrating AI models into routine clinical operations.
CUREUS JOURNAL OF MEDICAL SCIENCE
(2022)
Review
Dentistry, Oral Surgery & Medicine
Sanjeev B. Khanagar et al.
Summary: Artificial intelligence (AI) has made significant advancements in dentistry, with various applications widely employed for diagnosis and prediction tasks, showing excellent performance and accuracy.
JOURNAL OF DENTAL SCIENCES
(2021)
Article
Chemistry, Analytical
Song Hee Oh et al.
Summary: By comparing and analyzing conventional examination with the QLF technique, this study aimed to present an optimal diagnostic protocol. It was found that QLF showed higher sensitivity in detecting occlusal dental caries and cracks compared to the conventional method. The QLF technique may be a useful adjunct tool for the detection of occlusal caries and peripheral cracks.
Article
Oncology
Peter Grieco et al.
Summary: Bitewing radiographs are crucial in diagnosing carious lesions, with the lack of their use in Japan resulting in a large number of undiagnosed carious lesions and increased healthcare costs.
ANNALS OF TRANSLATIONAL MEDICINE
(2021)
Article
Computer Science, Artificial Intelligence
Natalia Norori et al.
Summary: Artificial intelligence has great potential in clinical decision making, but algorithmic bias is a major challenge that needs to be addressed. If training data does not represent population variability, AI is at risk of reinforcing bias, leading to serious consequences.
Review
Dentistry, Oral Surgery & Medicine
Nabilla Musri et al.
Summary: This study analyzed and reviewed deep learning convolutional neural networks for detecting and diagnosing early-stage dental caries on periapical radiographs. Findings suggest that these networks have the potential to enhance precision in detecting and diagnosing carious lesions, ultimately improving patient outcomes.
IMAGING SCIENCE IN DENTISTRY
(2021)
Review
Dentistry, Oral Surgery & Medicine
E. Bernabe et al.
JOURNAL OF DENTAL RESEARCH
(2020)
Article
Dentistry, Oral Surgery & Medicine
Rafal Obuchowicz et al.
Article
Dentistry, Oral Surgery & Medicine
Felix Kunz et al.
JOURNAL OF OROFACIAL ORTHOPEDICS-FORTSCHRITTE DER KIEFERORTHOPADIE
(2020)
Article
Dentistry, Oral Surgery & Medicine
F. Schwendicke et al.
JOURNAL OF DENTAL RESEARCH
(2020)
Article
Medicine, General & Internal
Michael G. Endres et al.
Review
Radiology, Nuclear Medicine & Medical Imaging
Michael Tran Duong et al.
BRITISH JOURNAL OF RADIOLOGY
(2019)
Review
Biochemistry & Molecular Biology
William H. Bowen et al.
TRENDS IN MICROBIOLOGY
(2018)
Article
Dentistry, Oral Surgery & Medicine
Jae-Hong Lee et al.
JOURNAL OF DENTISTRY
(2018)
Review
Radiology, Nuclear Medicine & Medical Imaging
Rikiya Yamashita et al.
INSIGHTS INTO IMAGING
(2018)
Article
Law
Christina Tikkinen-Piri et al.
COMPUTER LAW & SECURITY REVIEW
(2018)
Article
Dentistry, Oral Surgery & Medicine
N. J. Kassebaum et al.
JOURNAL OF DENTAL RESEARCH
(2017)
Article
Chemistry, Analytical
Peter de Boves Harrington
ANALYTICAL CHEMISTRY
(2015)
Article
Dentistry, Oral Surgery & Medicine
J. Gomez
