Deep learning for caries detection: A systematic review

• Published in: Journal of dentistry 2022-07, Vol.122, p.104115-104115, Article 104115
• Main Authors: Mohammad-Rahimi, Hossein, Motamedian, Saeed Reza, Rohban, Mohammad Hossein, Krois, Joachim, Uribe, Sergio E., Mahmoudinia, Erfan, Rokhshad, Rata, Nadimi, Mohadeseh, Schwendicke, Falk
• Format: Article
• Language: English
• Subjects: Accuracy; Artificial intelligence; Bias; Classification; Data collection; Datasets; Deep learning; Dental caries; Dentistry; Heterogeneity; I.R. radiation; Image quality; Image segmentation; Infrared imagery; Infrared photography; Lesions; Machine learning; Medical imaging; Meta-analysis; Neural networks; Object recognition; Optical Coherence Tomography; Performance measurement; Quality assessment; Quality control; Radiographs; Radiography; Systematic review; Tomography
• ISSN: 0300-5712; 1879-176X
• DOI: 10.1016/j.jdent.2022.104115

Detecting caries lesions is challenging for dentists, and deep learning models may help practitioners to increase accuracy and reliability. We aimed to systematically review deep learning studies on caries detection. We selected diagnostic accuracy studies that used deep learning models on dental imagery (including radiographs, photographs, optical coherence tomography images, near-infrared light transillumination images). The latest version of the quality assessment tool for diagnostic accuracy studies (QUADAS-2) tool was used for risk of bias assessment. Meta-analysis was not performed due to heterogeneity in the studies methods and their performance measurements. Databases (Medline via PubMed, Google Scholar, Scopus, Embase) and a repository (ArXiv) were screened for publications published after 2010, without any limitation on language. From 252 potentially eligible references, 48 studies were assessed full-text and 42 included, using classification (n = 26), object detection (n = 6), or segmentation models (n = 10). A wide range of performance metrics was used; image, object or pixel accuracy ranged between 68%-99%. The minority of studies (n = 11) showed a low risk of biases in all domains, and 13 studies (31.0%) low risk for concerns regarding applicability. The accuracy of caries classification models varied, i.e. 71% to 96% on intra-oral photographs, 82% to 99.2% on peri-apical radiographs, 87.6% to 95.4% on bitewing radiographs, 68.0% to 78.0% on near-infrared transillumination images, 88.7% to 95.2% on optical coherence tomography images, and 86.1% to 96.1% on panoramic radiographs. Pooled diagnostic odds ratios varied from 2.27 to 32,767. For detection and segmentation models, heterogeneity in reporting did not allow useful pooling. An increasing number of studies investigated caries detection using deep learning, with a diverse types of architectures being employed. Reported accuracy seems promising, while study and reporting quality are currently low. Deep learning models can be considered as an assistant for decisions regarding the presence or absence of carious lesions.