Diagnostic accuracy of dental caries detection using ensemble techniques in deep learning with intraoral camera images

PLOS ONE RESEARCH ARTICLE Diagnostic accuracy of dental caries detection using ensemble techniques in deep learning with intraoral camera images Sohee Kang1☯, Byungeun Shon2,3☯, Eun Young Park1, Sungmoon Jeong2,3, EunKyong Kim ID4* a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 1 Department of Dentistry, College of Medicine, Yeungnam University, Daegu, South Korea, 2 Research Center for Artificial Intelligence in Medicine, Kyungpook National University Hospital, Daegu, South Korea, 3 Department of Medical Informatics, School of Medicine, Kyungpook National University, Daegu, South Korea, 4 Department of Dental Hygiene, College of Science and Technology, Kyungpook National University, Sangju, South Korea ☯ These authors contributed equally to this work. * Abstract OPEN ACCESS Citation: Kang S, Shon B, Park EY, Jeong S, Kim EK (2024) Diagnostic accuracy of dental caries detection using ensemble techniques in deep learning with intraoral camera images. PLoS ONE 19(9): e0310004. https://doi.org/10.1371/journal. pone.0310004 Editor: Andrej M. Kielbassa, Danube Private University, AUSTRIA Received: November 2, 2023 Accepted: August 22, 2024 Published: September 6, 2024 Copyright: © 2024 Kang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: The dataset used in this study can not be shared publicly because of limited anonymity. This dataset can be available upon request for researchers who meet the criteria for access to confidential data (contact via and (FAX number: 82-054-530-1429)) of department of dental hygiene, Kyungpook National University), which was imposed by Institutional Review Board of Kyungpook National University (KNU-20210097). Camera image-based deep learning (DL) techniques have achieved promising results in dental caries screening. To apply the intraoral camera image-based DL technique for dental caries detection and assess its diagnostic performance, we employed the ensemble technique in the image classification task. 2,682 intraoral camera images were used as the dataset for image classification according to dental caries presence and caries-lesion localization using DL models such as ResNet-50, Inception-v3, Inception-ResNet-v2, and Faster R-convolutional neural network according to diagnostic study design. 534 participants whose mean age [SD] was 47.67 [±13.94] years were enrolled. The dataset was divided into training (56.0%), validation (14.0%), and test subset (30.0%) annotated by one experienced dentist as a reference standard about dental caries detection and lesion location. The confusion matrix, area under the receiver operating characteristic curve (AUROC), and average precision (AP) were evaluated for performance analysis. In the end-to-end dental caries image classification, the ensemble DL models had consistently improved performance, in which as the best results, the ensemble model of Inception-ResNet-v2 achieved 0.94 of AUROC and 0.97 of AP. On the other hand, the explainable model achieved 0.91 of AUROC and 0.96 of AP after the ensemble application. For dental caries classification using intraoral camera images, the application of ensemble techniques exhibited consistently improved performance regardless of the DL models. Furthermore, the trial to create an explainable DL model based on carious lesion detection yielded favorable results. 1. Introduction Dental caries is a disease that is common worldwide [1]. If it can be detected early, minimally invasive treatment is possible, which can contribute to tooth substance preservation more PLOS ONE | https://doi.org/10.1371/journal.pone.0310004 September 6, 2024 1 / 13 PLOS ONE Funding: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2020R1F1A1070070) and by the 2022 Yeungnam University Research Grant (No. 222A580015). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: NO authors have competing interests Dental caries and deep Learning using intraoral camera images conservatively and effectively [2]. For example, in case of proximal surface caries, the resin infiltration technique was reported to be effective in preserving tooth substance of both marginal ridge and proximal contact by itself or along with internal tunnel restoration [3–5]. Therefore, this technique was recommended as a minimally invasive treatment by preempting surgical intervention among some non-cavitated caries [3]. An effective screening method to achieve a quick and exact diagnosis of dental caries is useful for both the patients and the dentist. For this reason, an intraoral camera, which can show enlarged images of the tooth surface with a high resolution on a computer monitor, is commonly used along with radiographs at dental hospitals in Korea. The use of a convolutional neural network (CNN), a deep learning (DL) algorithm, is a very efficient method for image data processing [6–8]. With the application of CNN, the development of medical decision support systems has become a topic of interest in both the medical academia and industry [9]. In dentistry, there have been attempts to detect dental caries by using CNN models with various types of dental images [10–18], some of which were used to classify or localize dental caries lesions with dental X-ray images [10–18]. In other studies, dental caries lesions were classified using near-infrared light transmission illumination images [19, 20], or optical coherence tomography [21]. Periapical tooth lesions were also detected using images obtained by cone-beam computed tomography scans [22]. However, photographic images captured by an intraoral camera or smartphone having the advantage of convenience and safety are currently used for the application of an artificial intelligence (AI) model to screen dental caries in many studies [23–28] and demonstrated significant improvements in performance with various techniques [29–31]. For example, a previous study reported an accuracy of 0.81 and an area under the receiver operating characteristic curve (AUROC) of 0.84 using tooth surface segmentation in intraoral images [30]. Along with segmentation, ensemble techniques offer a way to enhance the performance of DL models by combining multiple models, each with its own strengths and weaknesses [32–34]. As this approach creates a more robust and accurate model, while also mitigating errors, improving generalization performance, and reducing overfitting, an ensemble model has been increasingly used in disease classification [35–37]. Thus, it is necessary to use an ensemble technique with intraoral camera images for dental caries classification for better performance. Fu (...truncated)