Application of Convolutional Neural Network in the Diagnosis of Jaw Tumors

Abstract

ObjectivesAmeloblastomas and keratocystic odontogenic tumors (KCOTs) are important odontogenic tumors of the jaw. While their radiological findings are similar, the behaviors of these two types of tumors are different. Precise preoperative diagnosis of these tumors can help oral and maxillofacial surgeons plan appropriate treatment. In this study, we created a convolutional neural network (CNN) for the detection of ameloblastomas and KCOTs.MethodsFive hundred digital panoramic images of ameloblastomas and KCOTs were retrospectively collected from a hospital information system, whose patient information could not be identified, and preprocessed by inverse logarithm and histogram equalization. To overcome the imbalance of data entry, we focused our study on 2 tumors with equal distributions of input data. We implemented a transfer learning strategy to overcome the problem of limited patient data. Transfer learning used a 16-layer CNN (VGG-16) of the large sample dataset and was refined with our secondary training dataset comprising 400 images. A separate test dataset comprising 100 images was evaluated to compare the performance of CNN with diagnosis results produced by oral and maxillofacial specialists.ResultsThe sensitivity, specificity, accuracy, and diagnostic time were 81.8%, 83.3%, 83.0%, and 38 seconds, respectively, for the CNN. These values for the oral and maxillofacial specialist were 81.1%, 83.2%, 82.9%, and 23.1 minutes, respectively.ConclusionsAmeloblastomas and KCOTs could be detected based on digital panoramic radiographic images using CNN with accuracy comparable to that of manual diagnosis by oral maxillofacial specialists. These results demonstrate that CNN may aid in screening for ameloblastomas and KCOTs in a substantially shorter time.

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

“…Their system was capable of detecting additional roots at a consistent performance level (Hiraiwa et al ). Poedjiastoeti & Suebnukarn () created a CNN to detect ameloblastomas and keratocystic odontogenic tumours, two of the most common dental tumours seen in the mandible. Whilst the sensitivity, specificity, accuracy and diagnostic time were 81.8%, 83.3%, 83.0% and 38 seconds, respectively, for the CNN, the oral and maxillofacial specialist matched the AI in all these parameters, except diagnostic time, which took 23.1 minutes.…”

“…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 ).…”

Radiomics and Machine Learning in Oral Healthcare

“…Consequently, automatic methods based on deep neural networks have been tested for several purposes, which are as follows: classification, image registration, segmentation, lesion detection, image retrieval, image guided therapy, image generation, and enhancement . Most recently, radiomics and AI research have been advancing in the dental field, revealing the potential of these technologies to substantially improve clinical care …”

“…When combining the term “artificial intelligence” and “radiology” and “dental” or “oral,” 196 articles were retrieved in Pubmed database. Some recent studies have demonstrated that CNN‐based methods may be used in dental images for several purposes, as demonstrated in Table …”

“…The most investigated applications of DL algorithms in DMFR were for the detection, classification, or diagnosis of diseases or anatomical structures, such as classification of teeth and mandibular morphology; differentiation of jaw tumors; and detection of root fractures, Sjögren's syndrome, maxillary sinusitis, calcified carotid atheroma's, caries, and periodontal diseases . However, the studies were heterogeneous and mainly based on 2D images (intraoral or extraoral radiographs).…”