A deep learning approach to automatic teeth detection and numbering based on object detection in dental periapical films

Abstract

We propose using faster regions with convolutional neural network features (faster R-CNN) in the TensorFlow tool package to detect and number teeth in dental periapical films. To improve detection precisions, we propose three post-processing techniques to supplement the baseline faster R-CNN according to certain prior domain knowledge. First, a filtering algorithm is constructed to delete overlapping boxes detected by faster R-CNN associated with the same tooth. Next, a neural network model is implemented to detect missing teeth. Finally, a rule-base module based on a teeth numbering system is proposed to match labels of detected teeth boxes to modify detected results that violate certain intuitive rules. The intersection-over-union (IOU) value between detected and ground truth boxes are calculated to obtain precisions and recalls on a test dataset. Results demonstrate that both precisions and recalls exceed 90% and the mean value of the IOU between detected boxes and ground truths also reaches 91%. Moreover, three dentists are also invited to manually annotate the test dataset (independently), which are then compared to labels obtained by our proposed algorithms. The results indicate that machines already perform close to the level of a junior dentist.

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

“…To demonstrate the system’s robust performance, the algorithms were compared to the responses of three dentists, who reviewed the data set independently. Chen et al () concluded the AI machines performed at a success rate close to that of a junior dentist.…”

“…Their results demonstrated that AI deep learning algorithms have potential for practical application within a clinical setting (Tuzoff et al ). Chen et al () presented a study to detect and number teeth in dental periapical films using faster regions with convolutional neural network features (faster R‐CNN) in the TensorFlow library. They used three post‐processing methods to integrate the basis of faster R‐CNN to improve detection predictions.…”

“…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

“…Faster regions with convolutional neural network features (faster R-CNN) in the TensorFlow tool package were used by Chen et al to detect and number the teeth in dental periapical films [16]. Here, 800 images were employed as the training dataset, 200 as the test dataset and 250 as the validation dataset.…”

“…There are several image techniques in the dentistry field depending on their use. Periapical images are employed to capture intact teeth, including front and posterior, as well as their surrounding bone; therefore, periapical images are very helpful to visualize the potential caries, periodontal bone loss and periapical diseases [16]. Bitewing images can only visualize the crowns of posterior teeth with simple layouts and considerably less overlaps [17].…”