Deep Learning for Automated Detection of Cyst and Tumors of the Jaw in Panoramic Radiographs

Abstract

Patients with odontogenic cysts and tumors may have to undergo serious surgery unless the lesion is properly detected at the early stage. The purpose of this study is to evaluate the diagnostic performance of the real-time object detecting deep convolutional neural network You Only Look Once (YOLO) v2—a deep learning algorithm that can both detect and classify an object at the same time—on panoramic radiographs. In this study, 1602 lesions on panoramic radiographs taken from 2010 to 2019 at Yonsei University Dental Hospital were selected as a database. Images were classified and labeled into four categories: dentigerous cysts, odontogenic keratocyst, ameloblastoma, and no lesion. Comparative analysis among three groups (YOLO, oral and maxillofacial surgeons, and general practitioners) was done in terms of precision, recall, accuracy, and F1 score. While YOLO ranked highest among the three groups (precision = 0.707, recall = 0.680), the performance differences between the machine and clinicians were statistically insignificant. The results of this study indicate the usefulness of auto-detecting convolutional networks in certain pathology detection and thus morbidity prevention in the field of oral and maxillofacial surgery.

Machine Learning and Intelligent Diagnostics in Dental and Orofacial Pain Management: A Systematic Review

“…The current study of 34 published documents identified 8 articles [ 5 , 6 , 12 – 15 , 31 , 39 ] that made direct comparisons between the diagnostic accuracy of machine learning models and human clinicians. Of the 15 points evaluated from the MI-CLAIM checklist, all but one paper [ 39 ] scored over 13.…”

“…Bone diseases [ 9 ], temporomandibular joint disorders [ 10 ], space infections [ 11 ], salivary gland disorders [ 12 , 13 ], and sinusitis [ 14 ] elicit pain. Furthermore, neuralgia and secondary sensory nerve compression due to growing cysts and tumors can elicit severe pain [ 15 , 16 ]. These conditions are categorized as common diseases and disorders that elicit dental and orofacial pain in the dental clinic [ 17 ].…”

Using deep learning to predict temporomandibular joint disc perforation based on magnetic resonance imaging

“…In addition, deep learning, a class of machine learning, is increasingly being applied in the field of diagnosis and prediction related to medical imaging, yielding impressive results 12 . Yang et al reported favorable result for automated detection of cyst and tumor of the jaw in panoramic images 13 . Lee et al reported that cephalometric images can be applied for differential diagnosis of orthognathic surgery and orthodontic treatment based on deep convolutional neural networks with 95.4 ~ 96.4% success rate 14 .…”

Differential diagnosis of ameloblastoma and odontogenic keratocyst by machine learning of panoramic radiographs

“…These features are objective, and the high-level features can be captured due to the depth of the network. At the same time, the ReLu activation layer in the network ensures that the classifier is nonlinear, which enhances the fitting ability of the classifier [ 18 ]. With the development of artificial intelligence algorithms, the field of tumor classification and recognition based on digital images has achieved great success.…”

“…Watanabe H et al mainly studied the classification of cyst-like lesions, divided into radicular cysts and other lesions [ 17 ]. Yang H et al classified dentigerous cysts, odontogenic keratocyst, ameloblastoma, and no lesion, using the YOLO network [ 18 ]. However, few studies focus on the classification of AB and OK using the deep learning method.…”