A deep-learning artificial intelligence system for assessment of root morphology of the mandibular first molar on panoramic radiography

Abstract

The distal root of the mandibular first molar occasionally has an extra root, which can directly affect the outcome of endodontic therapy. In this study, we examined the diagnostic performance of a deep learning system for classification of the root morphology of mandibular first molars on panoramic radiographs. Dental cone-beam CT (CBCT) was used as the gold standard. Methods: CBCT images and panoramic radiographs of 760 mandibular first molars from 400 patients who had not undergone root canal treatments were analyzed. Distal roots were examined on CBCT images to determine the presence of a single or extra root. Image patches of the roots were segmented from panoramic radiographs and applied to a deep learning system, and its diagnostic performance in the classification of root morphplogy was examined. Results: Extra roots were observed in 21.4% of distal roots on CBCT images. The deep learning system had diagnostic accuracy of 86.9% for the determination of whether distal roots were single or had extra roots. conclusions: The deep learning system showed high accuracy in the differential diagnosis of a single or extra root in the distal roots of mandibular first molars.

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

“…Hiraiwa et al () evaluated the diagnostic performance of a deep learning system viewing panoramic radiographs to assess the number of distal roots present on mandibular first molars‐based training through CBCT findings. Their system was capable of detecting additional roots at a consistent performance level (Hiraiwa et al ).…”

“…Hiraiwa et al () evaluated the diagnostic performance of a deep learning system viewing panoramic radiographs to assess the number of distal roots present on mandibular first molars‐based training through CBCT findings. 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.…”

“…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 …”