Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm.

By Jae-Hong Lee, Do-Hyung Kim, Seong-Nyum Jeong, Seong-Ho Choi. E -journal of dentistry, 2018

832 citations recorded by the source corpus.

OBJECTIVES Deep convolutional neural networks (CNNs) are a rapidly emerging new area of medical research, and have yielded impressive results in diagnosis and prediction in the fields of radiology and pathology. The aim of the current study was to evaluate the efficacy of deep CNN algorithms for detection and diagnosis of dental caries on periapical radiographs. MATERIALS AND METHODS A total of 3000 periapical radiographic images were divided into a training and validation dataset (n = 2400 [80%]) and a test dataset (n = 600 [20%]). A pre-trained GoogLeNet Inception v3 CNN network was used for preprocessing and transfer learning. The diagnostic accuracy, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic (ROC) curve, and area under the curve (AUC) were calculated for detection and diagnostic performance of the deep CNN algorithm. RESULTS The diagnostic accuracies of premolar, molar, and both premolar and molar models were 89.0% (80.4-93.3), 88.0% (79.2-93.1), and 82.0% (75.5-87.1), respectively. The deep CNN algorithm achieved an AUC of 0.917 (95% CI 0.860-0.975) on premolar, an AUC of 0.890 (95% CI 0.819-0.961) on molar, and an AUC of 0.845 (95% CI 0.790-0.901) on both premolar and molar models. The premolar model provided the best AUC, which was significantly greater than those for other models (P < 0.001). CONCLUSIONS This study highlighted the potential utility of deep CNN architecture for the detection and diagnosis of dental caries. A deep CNN algorithm provided considerably good performance in detecting dental caries in periapical radiographs. CLINICAL SIGNIﬁCANCE: Deep CNN algorithms are expected to be among the most effective and efficient methods for diagnosing dental caries.

Subfields: Oral Surgery, Periodontics

View Paper: https://www.semanticscholar.org/paper/6114ffe861be0814f94fa3eeb0f45563998d2f16

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Citations in the corpus

The use and performance of artificial intelligence applications in dental and maxillofacial radiology: A systematic review. (2019, neutral): “With regard to the methods used for validating the performance of the AI models, 13 studie s 29,32,35,39,42,46,51,57,65,70,72–74 used split sample validation, ten 28,30,41,48,50,52,54,62,63,66 used leave- one-out cross-validation (LOOCV), two 34,68 used 3-fold cross-validation (CV), one 56 used…”
Survey on Deep Neural Networks in Speech and Vision Systems (2019, background): “…in deep neural networks have shown tremendous potential in almost all areas of medical imaging such as ophthalmology [215], dental radiography [216], skin cancer imaging [217], brain imaging [218], cardiac imaging [219, 220], urology [221], lung imaging [222], stroke imaging [223], and so on.”
A scoping review of transfer learning research on medical image analysis using ImageNet (2020, neutral): “M o d el C a te g o ry Inception [27,39–46] [47–51] [52,53] [54–59] [60,61] [62–65] [66] [67] VGGNet [68–73] [74–76] [77–81] [82–84] [85–87] [88] [89– 91] [92– 94] ResNet [95–97] [98– 101] [102– 105] [106] [107–109] [110,111] [112] [113]”
Developments, application, and performance of artificial intelligence in dentistry – A systematic review (2020, methodology): “AI technology in the detection of dental caries has demonstrated excellent results which were reﬂected in the study done by Lee et al., 26 who reported applying of CNN algorithms for detection and diagnosis of dental caries on periapical radiographs.”