Caries Detection on Intraoral Images Using Artificial Intelligence. (February 2022)

• Record Type: Journal Article
• Authors: Kühnisch, J.; Meyer, O.; Hesenius, M.; Hickel, R.; Gruhn, V.
• Is Part Of: Journal of dental research. Volume 101:Number 2(2022)
• Page Start: 158
• Page End: 165
• Publication Date: 2022-02
• Subjects: caries diagnostics -- caries assessment -- visual examination -- clinical evaluation -- convolutional neural networks -- deep learning; Dentistry -- Periodicals; Dentistry -- Social aspects -- Periodicals; Dentistry -- Periodicals; Research -- Periodicals; 617.6005
• DOI: 10.1177/00220345211032524
• Languages: English
• ISSNs: 0022-0345

Abstract

Although visual examination (VE) is the preferred method for caries detection, the analysis of intraoral digital photographs in machine-readable form can be considered equivalent to VE. While photographic images are rarely used in clinical practice for diagnostic purposes, they are the fundamental requirement for automated image analysis when using artificial intelligence (AI) methods. Considering that AI has not been used for automatic caries detection on intraoral images so far, this diagnostic study aimed to develop a deep learning approach with convolutional neural networks (CNNs) for caries detection and categorization (test method) and to compare the diagnostic performance with respect to expert standards. The study material consisted of 2, 417 anonymized photographs from permanent teeth with 1, 317 occlusal and 1, 100 smooth surfaces. All the images were evaluated into the following categories: caries free, noncavitated caries lesion, or caries-related cavitation. Each expert diagnosis served as a reference standard for cyclic training and repeated evaluation of the AI methods. The CNN was trained using image augmentation and transfer learning. Before training, the entire image set was divided into a training and test set. Validation was conducted by selecting 25%, 50%, 75%, and 100% of the available images from the training set. The statistical analysis included calculations of the sensitivity (SE), specificity (SP), and area under the receiver operating characteristic (ROC) curve (AUC). The CNN was able to correctly detect caries in 92.5% of cases when all test images were considered (SE, 89.6; SP, 94.3; AUC, 0.964). If the threshold of caries-related cavitation was chosen, 93.3% of all tooth surfaces were correctly classified (SE, 95.7; SP, 81.5; AUC, 0.955). It can be concluded that it was possible to achieve more than 90% agreement in caries detection using the AI method with standardized, single-tooth photographs. Nevertheless, the current approach needs further improvement.