Importance of bitewing radiographs for the early detection of interproximal carious lesions and the impact on healthcare expenditure in Japan

Grieco, Peter C.; Jivraj, Ashiana; Silva, John Da; Kuwajima, Yukinori; Ishida, Yoshiki; Ogawa, Kenji; Ohyama, Hiroe; Ishikawa-Nagai, Shigemi

doi:10.21037/atm-21-2197

Cited by 8 publications

(4 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With respect to dental images modalities, the studies included in our analysis used periapical radiographs, bitewings radiographs and orthopantomography for CLs detection. Bitewing radiographs have been shown to be the best diagnostic tool for the detection of interproximal dental caries [ 45 ]. In fact, nearly half of the studies in our analysis detected CLs in bitewing radiograph images.…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence for radiographic imaging detection of caries lesions: a systematic review

Albano,

Galiano,

Basile

et al. 2024

BMC Oral Health

View full text Add to dashboard Cite

Background The aim of this systematic review is to evaluate the diagnostic performance of Artificial Intelligence (AI) models designed for the detection of caries lesion (CL). Materials and methods An electronic literature search was conducted on PubMed, Web of Science, SCOPUS, LILACS and Embase databases for retrospective, prospective and cross-sectional studies published until January 2023, using the following keywords: artificial intelligence (AI), machine learning (ML), deep learning (DL), artificial neural networks (ANN), convolutional neural networks (CNN), deep convolutional neural networks (DCNN), radiology, detection, diagnosis and dental caries (DC). The quality assessment was performed using the guidelines of QUADAS-2. Results Twenty articles that met the selection criteria were evaluated. Five studies were performed on periapical radiographs, nine on bitewings, and six on orthopantomography. The number of imaging examinations included ranged from 15 to 2900. Four studies investigated ANN models, fifteen CNN models, and two DCNN models. Twelve were retrospective studies, six cross-sectional and two prospective. The following diagnostic performance was achieved in detecting CL: sensitivity from 0.44 to 0.86, specificity from 0.85 to 0.98, precision from 0.50 to 0.94, PPV (Positive Predictive Value) 0.86, NPV (Negative Predictive Value) 0.95, accuracy from 0.73 to 0.98, area under the curve (AUC) from 0.84 to 0.98, intersection over union of 0.3–0.4 and 0.78, Dice coefficient 0.66 and 0.88, F1-score from 0.64 to 0.92. According to the QUADAS-2 evaluation, most studies exhibited a low risk of bias. Conclusion AI-based models have demonstrated good diagnostic performance, potentially being an important aid in CL detection. Some limitations of these studies are related to the size and heterogeneity of the datasets. Future studies need to rely on comparable, large, and clinically meaningful datasets. Protocol PROSPERO identifier: CRD42023470708

show abstract

Section: Discussionmentioning

confidence: 99%

Artificial intelligence for radiographic imaging detection of caries lesions: a systematic review

Albano,

Galiano,

Basile

et al. 2024

BMC Oral Health

View full text Add to dashboard Cite

show abstract

“…Radiographic examinations, particularly dental bitewing X-rays, are widely used to diagnose dental caries [10]. They offer the ability to visualize both the surface and subsurface structures of teeth, enabling the detection of caries that might be hidden from the naked eye or in areas like interproximal spaces, which are hard to inspect visually.…”

Section: Radiographic Examinationmentioning

confidence: 99%

Revolutionizing Dental Caries Diagnosis through Artificial Intelligence

Anil,

Sudeep,

Saratchandran

et al. 2024

Dental Caries Perspectives - A Collection of Thoughtful Essays

View full text Add to dashboard Cite

The diagnosis and management of dental caries, a prevalent global oral health issue, have traditionally depended on clinical examination and the interpretation of radiographic images. However, with the rapid advancements in technology, the landscape of dental diagnostics is transforming. This chapter delves into the revolutionary impact of artificial intelligence (AI) on detecting and managing dental caries. Dental professionals can now achieve enhanced diagnostic accuracy by harnessing the power of machine learning algorithms and image recognition technologies, even identifying early-stage caries that conventional methods might overlook. The integration of AI into dentistry not only promises improved patient outcomes by facilitating timely interventions and streamlining clinical workflows, potentially redefining the future of oral healthcare. While the prospects are promising, it is imperative to concurrently address the challenges and ethical considerations accompanying AI-driven diagnostics to ensure that the technology augments, rather than supplants, the expertise of dental professionals. The chapter serves as a comprehensive overview of the current state of AI in dental caries diagnosis, its potential benefits, and the road ahead.

show abstract

“…Detecting dental caries at the initial stages can avert the need for invasive procedures and significantly reduce healthcare expenses [ 5 ]. However, identifying initial proximal caries solely through clinical examinations is challenging, making bitewing radiography a valuable tool, as it is considered the gold standard for diagnosing demineralized proximal caries.…”

Section: Introductionmentioning

confidence: 99%

Transforming Dental Caries Diagnosis Through Artificial Intelligence-Based Techniques

Anil¹,

Porwal²,

Porwal³

2023

Cureus

View full text Add to dashboard Cite

Diagnosing dental caries plays a pivotal role in preventing and treating tooth decay. However, traditional methods of diagnosing caries often fall short in accuracy and efficiency. Despite the endorsement of radiography as a diagnostic tool, the identification of dental caries through radiographic images can be influenced by individual interpretation. Incorporating artificial intelligence (AI) into diagnosing dental caries holds significant promise, potentially enhancing the precision and efficiency of diagnoses. This review introduces the fundamental concepts of AI, including machine learning and deep learning algorithms, and emphasizes their relevance and potential contributions to the diagnosis of dental caries. It further explains the process of gathering and pre-processing radiography data for AI examination. Additionally, AI techniques for dental caries diagnosis are explored, focusing on image processing, analysis, and classification models for predicting caries risk and severity. Deep learning applications in dental caries diagnosis using convolutional neural networks are presented. Furthermore, the integration of AI systems into dental practice is discussed, including the challenges and considerations for implementation as well as ethical and legal aspects. The breadth of AI technologies and their prospective utility in clinical scenarios for diagnosing dental caries from dental radiographs is presented. This review outlines the advancements of AI and its potential in revolutionizing dental caries diagnosis, encouraging further research and development in this rapidly evolving field.

show abstract

Importance of bitewing radiographs for the early detection of interproximal carious lesions and the impact on healthcare expenditure in Japan

Cited by 8 publications

References 24 publications

Artificial intelligence for radiographic imaging detection of caries lesions: a systematic review

Artificial intelligence for radiographic imaging detection of caries lesions: a systematic review

Revolutionizing Dental Caries Diagnosis through Artificial Intelligence

Transforming Dental Caries Diagnosis Through Artificial Intelligence-Based Techniques

Contact Info

Product

Resources

About