Deep Learning Hybrid Method to Automatically Diagnose Periodontal Bone Loss and Stage Periodontitis

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.

The relationship between COVID-19 and the dental damage stage determined by radiological examination

“…RBL measurement is performed by evaluating the quantitative and qualitative perspectives of imaginable interproximal bone, which includes the distance between the cement-enamel junction (CEJ) and the most coronal level, where the periodontal ligament interval is considered to have a normal width [ 12 ]. Both RBL and %RBL classifications are related to the loss of bone tissue the teeth are connected, resulting in spontaneous loss of the teeth in the future [ 10 , 13 ]. All these classifications have emerged from an effort to find an accurate indicator of the oral and dental health status.…”

Deep learning based prediction of extraction difficulty for mandibular third molars

“…CNNs have shown excellent results in the analysis of radiographic images when compared to the results by medical experts. Previous studies have shown that deep learning can be used to recognize anatomical structures, find anomalies, measure the distance, and classify structures in medical images 1,[3][4][5][6][7][8][9][10][11][12][13][14][15] . However, in most studies, object detection was conducted manually, and tasks were limited to performing simple measurements, comparisons, or classifications.…”

“…In recent years, CNNs have been extensively used in many fields. In the healthcare industry, numerous studies have reported that a CNN can be used to analyze and diagnose medical images [1][2][3][4][5][6][7] . CNNs have also been used to better interpret the complexities of medical imaging by revealing patterns in large numbers of data and acquiring essential information to gain more knowledge 2 .…”

“…In the field of dentistry, CNNs have been applied for the detection of carious lesions, periodontal lesions, mandibular canals, cysts, and tumors 1,3,4,[7][8][9] . They are also used to assess the difficulty of realizing endodontic treatment, skeletal classification, soft tissue profiling, osteoporosis, root morphology evaluation, and survival prediction of oral cancer patients 5,[10][11][12][13][14] .…”

Barriers and Enablers for Artificial Intelligence in Dental Diagnostics: A Qualitative Study

“…In dental radiology, AI has been used for a range of purposes. Detection of dental caries [ 14 , 15 , 16 , 17 ], apical lesions [ 18 ], periodontal bone loss [ 19 , 20 , 21 ], tooth fractures [ 22 ] or sinusitis [ 23 ] is already possible with the use of AI. The reported accuracies for most of these tasks are promising, and the first AI tools for dental diagnostics are currently entering the market.…”