Abstract
We developed an automatic method for staging periodontitis on dental panoramic radiographs using the deep learning hybrid method. A novel hybrid framework was proposed to automatically detect and classify the periodontal bone loss of each individual tooth. The framework is a hybrid of deep learning architecture for detection and conventional CAD processing for classification. Deep learning was used to detect the radiographic bone level (or the CEJ level) as a simple structure for the whole jaw on panoramic radiographs. Next, the percentage rate analysis of the radiographic bone loss combined the tooth long-axis with the periodontal bone and CEJ levels. Using the percentage rate, we could automatically classify the periodontal bone loss. This classification was used for periodontitis staging according to the new criteria proposed at the 2017 World Workshop on the Classification of Periodontal and Peri-Implant Diseases and Conditions. The Pearson correlation coefficient of the automatic method with the diagnoses by radiologists was 0.73 overall for the whole jaw (p < 0.01), and the intraclass correlation value 0.91 overall for the whole jaw (p < 0.01). The novel hybrid framework that combined deep learning architecture and the conventional CAD approach demonstrated high accuracy and excellent reliability in the automatic diagnosis of periodontal bone loss and staging of periodontitis.
Highlights
Periodontal diseases, including gingivitis and periodontitis, are some of the most common diseases that mankind faces
We proposed a novel hybrid framework of deep learning architecture and the conventional Computer-aided diagnosis (CAD) approach to detect and classify periodontal bone loss according to the 2017 World Workshop criteria[4]
The stage related to the severity and extent of periodontitis should be determined using the clinical attachment loss (CAL), or radiographic bone loss (RBL) if the CAL is not available[4]
Summary
Periodontal diseases, including gingivitis and periodontitis, are some of the most common diseases that mankind faces. Computer-aided diagnosis (CAD) has been used to identify cavities and periodontitis lesions, as well as maxillary sinusitis, osteoporosis, and other pathologies in the oral and maxillofacial field[8] It can provide dental professionals with a valuable second opinion by automatically detecting and classifying pathological changes. CNN-based methods were proposed for detecting radiographic bone loss (RBL) on dental panoramic radiographs[21,22] These methods only detected the region that showed RBL, and could not quantify or classify it in order to stage the periodontitis. The aim of this study was to develop an automated method for diagnosing periodontal bone loss (of individual teeth) for staging the periodontitis on dental panoramic radiographs using the deep learning hybrid method for the first time. We proposed a novel hybrid framework of deep learning architecture and the conventional CAD approach to detect and classify periodontal bone loss according to the 2017 World Workshop criteria[4]
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