Novel technique for caries detection using curvilinear semantic deep convolutional neural network

Rajee, M. V.; Mythili, C.

doi:10.1007/s11042-022-13789-w

Cited by 4 publications

(2 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It entails creating neural network models that can efficiently process dental radiographs to estimate an individual's age correctly. The choice of design is crucial to capture the complex patterns and temporal correlations visible in these radiography pictures [22]. The choice of appropriate tiers and units for the neural network is a key factor.…”

Section: Deep Learning Architectures For Dental Age Estimationmentioning

confidence: 99%

Deep Recurrent Neural Network-Based Assessment of Human Dental Age and Gender from Dental Radiographs

B. Hemalatha

2023

J. Adv. Zool.

View full text Add to dashboard Cite

The First Chapter's introduction discussed the ability to determine a person's gender and dental age with great accuracy and efficiency is made possible by this technology. It has also done a study that aims to leverage the groundbreaking advantages of deep learning in the dental age and gender evaluation by providing an accurate and automated approach that goes beyond the constraints of traditional methods. The Second Chapter's Literature Review explained Deep Learning Applications in Dental Radiography and Traditional Methods for Dental Age and Gender Assessment and Datasets and Annotations for Dental Radiographs. It has also done Temporal Dependencies in Dental Radiographs. The Third Chapter Methodology discussed that dental radiography data contains rich environmental information that necessitates a nuanced comprehension; this study employs an interpretivist research philosophy. It has also been done to examine pre-existing ideas and models in the context of tooth age and gender evaluation, a deductive approach is used in this study.

show abstract

Section: Deep Learning Architectures For Dental Age Estimationmentioning

confidence: 99%

Deep Recurrent Neural Network-Based Assessment of Human Dental Age and Gender from Dental Radiographs

B. Hemalatha

2023

J. Adv. Zool.

View full text Add to dashboard Cite

show abstract

“…For improving dental disease diagnosis and treatment plans, deep learning [7] has significantly influenced the processing of intraoral X-ray images in image processing [8], segmentation [9][10][11][12] and enhancements [13][14][15]. These developments integrating intraoral X-ray imaging with deep learning techniques enhance the precision of oral health condition recognition and detection, such as dental caries [16][17][18][19][20][21][22], implant [23], oriented tooth [24], restoration by filling [25] and dental material [26]. This study adapts deep learning techniques on a novel dataset to recognize and detect twenty categories as abscessed teeth, calculus, caries, cysts, dental bridges, dental crowns, extracted teeth, filling overhang, impacted wisdom teeth, implants, mesialized dentition, mixed dentition, periodontal bone loss, pulpitis, restoration by filling, retained root, screw-retained restoration, single-root canal treatment, two-root canal treatment and three-root canal treatment.…”

Section: Introductionmentioning

confidence: 99%

CVApool: using null-space of CNN weights for the tooth disease classification

Can,

Isik,

Anagun

2024

Neural Comput & Applic

View full text Add to dashboard Cite

In light of current developments in dental care, dental professionals have increasingly used deep learning methods to get precise diagnoses of oral problems. Using intraoral X-rays in dental radiography is imperative in many dental interventions. Integrating deep learning techniques with a unique collection of intraoral X-ray images has been undertaken to enhance the accuracy of dental disease detection. In this study, we propose an alternative pooling layer, namely the Common Vector Approach Pooling technique, to address the constraints associated with average pooling in deep learning methods. The experiments are conducted on a large dataset, involving twenty different dental conditions, divided into seven categories. Our proposed approach achieved a high accuracy rate of 86.4% in identifying dental problems across the seven oral categories.

show abstract

Primary Methods and Algorithms in Artificial-Intelligence-Based Dental Image Analysis: A Systematic Review

Bonny,

Al Nassan,

Obaideen

et al. 2024

Algorithms

View full text Add to dashboard Cite

Artificial intelligence (AI) has garnered significant attention in recent years for its potential to revolutionize healthcare, including dentistry. However, despite the growing body of literature on AI-based dental image analysis, challenges such as the integration of AI into clinical workflows, variability in dataset quality, and the lack of standardized evaluation metrics remain largely underexplored. This systematic review aims to address these gaps by assessing the extent to which AI technologies have been integrated into dental specialties, with a specific focus on their applications in dental imaging. A comprehensive review of the literature was conducted, selecting relevant studies through electronic searches from Scopus, Google Scholar, and PubMed databases, covering publications from 2018 to 2023. A total of 52 articles were systematically analyzed to evaluate the diverse approaches of machine learning (ML) and deep learning (DL) in dental imaging. This review reveals that AI has become increasingly prevalent, with researchers predominantly employing convolutional neural networks (CNNs) for detection and diagnosis tasks. Pretrained networks demonstrate strong performance in many scenarios, while ML techniques have shown growing utility in estimation and classification. Key challenges identified include the need for larger, annotated datasets and the translation of research outcomes into clinical practice. The findings underscore AI’s potential to significantly advance diagnostic support, particularly for non-specialist dentists, improving patient care and clinical efficiency. AI-driven software can enhance diagnostic accuracy, facilitate data sharing, and support collaboration among dental professionals. Future developments are anticipated to enable patient-specific optimization of restoration designs and implant placements, leveraging personalized data such as dental history, tissue type, and bone thickness to achieve better outcomes.

show abstract

Novel technique for caries detection using curvilinear semantic deep convolutional neural network

Cited by 4 publications

References 28 publications

Deep Recurrent Neural Network-Based Assessment of Human Dental Age and Gender from Dental Radiographs

Deep Recurrent Neural Network-Based Assessment of Human Dental Age and Gender from Dental Radiographs

CVApool: using null-space of CNN weights for the tooth disease classification

Primary Methods and Algorithms in Artificial-Intelligence-Based Dental Image Analysis: A Systematic Review

Contact Info

Product

Resources

About