Deep Learning for the Radiographic Detection of Periodontal Bone Loss

Abstract

We applied deep convolutional neural networks (CNNs) to detect periodontal bone loss (PBL) on panoramic dental radiographs. We synthesized a set of 2001 image segments from panoramic radiographs. Our reference test was the measured % of PBL. A deep feed-forward CNN was trained and validated via 10-times repeated group shuffling. Model architectures and hyperparameters were tuned using grid search. The final model was a seven-layer deep neural network, parameterized by a total number of 4,299,651 weights. For comparison, six dentists assessed the image segments for PBL. Averaged over 10 validation folds the mean (SD) classification accuracy of the CNN was 0.81 (0.02). Mean (SD) sensitivity and specificity were 0.81 (0.04), 0.81 (0.05), respectively. The mean (SD) accuracy of the dentists was 0.76 (0.06), but the CNN was not statistically significant superior compared to the examiners (p = 0.067/t-test). Mean sensitivity and specificity of the dentists was 0.92 (0.02) and 0.63 (0.14), respectively. A CNN trained on a limited amount of radiographic image segments showed at least similar discrimination ability as dentists for assessing PBL on panoramic radiographs. Dentists’ diagnostic efforts when using radiographs may be reduced by applying machine-learning based technologies.

Evaluation of artificial intelligence for detecting periapical pathosis on cone‐beam computed tomography scans

“…Krois et al () designed deep CNNs to detect periodontal bone loss on panoramic radiographs. A CNN trained on a limited amount of image segments showed discrimination ability similar to that of live dentists assessing periodontal bone loss with panoramic radiographs (Krois et al ).…”

“…Krois et al () designed deep CNNs to detect periodontal bone loss on panoramic radiographs. A CNN trained on a limited amount of image segments showed discrimination ability similar to that of live dentists assessing periodontal bone loss with panoramic radiographs (Krois et al ). Johari et al () modelled a probabilistic neural network (PNN) to detect vertical root fractures in vital and endodontically treated teeth using periapical and CBCT radiographs.…”

“…CNNs have been successfully used for automatic assessment of various medical and dental problems, including image‐based automated diagnosis to detect lung and brain lesions (Akkus et al , Song et al , Wang et al , Blanc‐Durand et al ), breast cancer in mammography images (Becker et al ), colorectal polyps and prostate cancer (Wang et al , Byrne et al ), skin cancer (Esteva et al ), diabetic retinopathy in retinal fundus photographs (Gulshan et al ), hip osteoarthritis (Xue et al ) and bone age assessment (Lee et al ). In dentistry, CNNs have been applied to detect carious lesions, periapical lesions, tooth eruption and numbering, vertical root fractures, assess root morphology or periodontal bone loss, dental and jaw pathosis, osteoporosis, and maxillary sinusitis on dental radiographs (Kositbowornchai et al , Miki et al , Ezhov et al , Murata et al , Poedjiastoeti & Suebnukarn , Lee et al ,b, Zakirov et al , Zakirov et al , Chen et al , Ekert et al , Hiraiwa et al , Hwang et al , Krois et al , Tuzoff et al ).…”

Dental Images Recognition Technology and Applications: A Literature Review

“…CNNs have been recently applied in dentistry to detect periodontal bone loss [11,12], caries on bitewing radiographs [13], apical lesions [14], or for medical image classification [12]. These kinds of neural networks can be used to detect structures, such as teeth or caries, to classify them and to segment them [15].…”

Association of Glycated Proteins with Inflammatory Proteins and Periodontal Disease Parameters

“…The extent of hemoglobin glycation is influenced by the concentration of glucose in the blood. Based on the life span of erythrocytes (~120 days), HbA1c, therefore, reflects the mean glucose concentration over the preceding [8][9][10][11][12] week period [5]. HbA1c is a validated and reliable marker for determining hyperglycemia and predicting complications related to it [6].…”