On using convolutional neural networks to classify periodontal bone destruction in periapical radiographs

Morán, Maira Beatriz Hernández; Faria, Marcelo Daniel Brito; Giraldi, Gilson A.; Bastos, Luciana Freitas; Inacio, Bruno da Silva; Conci, Aura

doi:10.1109/bibm49941.2020.9313501

Cited by 17 publications

(10 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Automatic feature segmentation of common radiographic abnormalities, including alveolar bone loss, interradicular radiolucency and dental caries was achieved by using DL-based networks such U-Net, Segnet, XNet, U-Net +, and Densenet [ 80 ]. CNNs were also used to identify locations in periapical exams based on the presence of periodontal bone loss [ 81 ] and other dental disease detection [ 82 ] indicators. Tajinda et al integrated segmentation and classification tasks for grading periodontitis from periapical radiography images to create the hybrid network for periodontitis stages from radiograph (HYNETS) end-to-end DL network.…”

Section: Resultsmentioning

confidence: 99%

“…Different types of images were used by different researchers based on the techniques they used in DI. Radiographic images [ 16 , 41 , 43 , 56 , 63 , 64 , 65 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 87 , 157 ], near-infrared light transillumination (NILT) [ 88 , 89 , 90 ], intraoral images [ 66 , 86 , 91 , 92 , 93 , 95 , 96 , 97 , 158 , 159 , 160 ], 3D model [ 102 , 113 , 114 , 115 , 161 ] were used in the research for dental diseases diagnostic on the 3D dental model. The studies on the dental disease’s diagnostic on the CBCT dental model used the CT images [ 124 ], 3D CT scans […”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of Deep Learning Techniques for Dental Informatics: A Systematic Literature Review

et al. 2022

View full text Add to dashboard Cite

Within the ever-growing healthcare industry, dental informatics is a burgeoning field of study. One of the major obstacles to the health care system’s transformation is obtaining knowledge and insightful data from complex, high-dimensional, and diverse sources. Modern biomedical research, for instance, has seen an increase in the use of complex, heterogeneous, poorly documented, and generally unstructured electronic health records, imaging, sensor data, and text. There were still certain restrictions even after many current techniques were used to extract more robust and useful elements from the data for analysis. New effective paradigms for building end-to-end learning models from complex data are provided by the most recent deep learning technology breakthroughs. Therefore, the current study aims to examine the most recent research on the use of deep learning techniques for dental informatics problems and recommend creating comprehensive and meaningful interpretable structures that might benefit the healthcare industry. We also draw attention to some drawbacks and the need for better technique development and provide new perspectives about this exciting new development in the field.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Analysis of Deep Learning Techniques for Dental Informatics: A Systematic Literature Review

et al. 2022

View full text Add to dashboard Cite

show abstract

“…In the last years, solutions based on Artificial Intelligence (AI) algorithms, especially deep-learning ones, have emerged in a wide range of application fields, demonstrating outstanding results. This trend is also perceptible in Dental Medicine [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. Considering the analysis of radiographs as a complementary tool for diagnosis, the use of Convolutional Neural Networks (CNNs) to aid in the identification of several lesions has shown promising results [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Classification of Approximal Caries in Bitewing Radiographs Using Convolutional Neural Networks

Morán

Faria

Giraldi

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

Dental caries is an extremely common problem in dentistry that affects a significant part of the population. Approximal caries are especially difficult to identify because their position makes clinical analysis difficult. Radiographic evaluation—more specifically, bitewing images—are mostly used in such cases. However, incorrect interpretations may interfere with the diagnostic process. To aid dentists in caries evaluation, computational methods and tools can be used. In this work, we propose a new 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. For this study, we acquired 112 bitewing radiographs. From these exams, we extracted individual tooth images from each exam, applied a data augmentation process, and used the resulting images to train CNN classification models. The tooth images were previously labeled by experts to denote the defined classes. We evaluated classification models based on the Inception and ResNet architectures using three different learning rates: 0.1, 0.01, and 0.001. The training process included 2000 iterations, and the best results were achieved by the Inception model with a 0.001 learning rate, whose accuracy on the test set was 73.3%. The results can be considered promising and suggest that the proposed method could be used to assist dentists in the evaluation of bitewing images, and the definition of lesion severity and appropriate treatments.

show abstract

“…Although the maturity of AI in the field of dentistry has lagged in several subfields such as periodontology, endodontics, orthodontics, restorative dentistry, and oral pathology, there has been a large interest in the past few years as artificial intelligence has become increasingly accessible to researchers. AI has made substantial progress in the diverse disciplines of dentistry including dental disease diagnosis [ 5 ], localization [ 6 ], classification [ 7 ], estimation [ 8 ], and assessment of dental disease [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Advancements in Dentistry with Artificial Intelligence: Current Clinical Applications and Future Perspectives

et al. 2022

View full text Add to dashboard Cite

Artificial intelligence has been widely used in the field of dentistry in recent years. The present study highlights current advances and limitations in integrating artificial intelligence, machine learning, and deep learning in subfields of dentistry including periodontology, endodontics, orthodontics, restorative dentistry, and oral pathology. This article aims to provide a systematic review of current clinical applications of artificial intelligence within different fields of dentistry. The preferred reporting items for systematic reviews (PRISMA) statement was used as a formal guideline for data collection. Data was obtained from research studies for 2009–2022. The analysis included a total of 55 papers from Google Scholar, IEEE, PubMed, and Scopus databases. Results show that artificial intelligence has the potential to improve dental care, disease diagnosis and prognosis, treatment planning, and risk assessment. Finally, this study highlights the limitations of the analyzed studies and provides future directions to improve dental care.

show abstract

On using convolutional neural networks to classify periodontal bone destruction in periapical radiographs

Cited by 17 publications

References 13 publications

Analysis of Deep Learning Techniques for Dental Informatics: A Systematic Literature Review

Analysis of Deep Learning Techniques for Dental Informatics: A Systematic Literature Review

Classification of Approximal Caries in Bitewing Radiographs Using Convolutional Neural Networks

Advancements in Dentistry with Artificial Intelligence: Current Clinical Applications and Future Perspectives

Contact Info

Product

Resources

About