Automatic Detection of Mandibular Fractures in Panoramic Radiographs Using Deep Learning

Abstract

Mandibular fracture is one of the most frequent injuries in oral and maxillo-facial surgery. Radiologists diagnose mandibular fractures using panoramic radiography and cone-beam computed tomography (CBCT). Panoramic radiography is a conventional imaging modality, which is less complicated than CBCT. This paper proposes the diagnosis method of mandibular fractures in a panoramic radiograph based on a deep learning system without the intervention of radiologists. The deep learning system used has a one-stage detection called you only look once (YOLO). To improve detection accuracy, panoramic radiographs as input images are augmented using gamma modulation, multi-bounding boxes, single-scale luminance adaptation transform, and multi-scale luminance adaptation transform methods. Our results showed better detection performance than the conventional method using YOLO-based deep learning. Hence, it will be helpful for radiologists to double-check the diagnosis of mandibular fractures.

A novel system applying artificial intelligence in the identification of air leak sites

“…In the previously reported outcomes in the prototype application in radiology and pathology, precision and recall are approximately 87% to 93% and 65% to 88%, respectively. 4 , 8 Compared with previous studies, the precision score of the current model is slightly lower. This indicates that it is detecting regions other than the leak sites, creating FP cases.…”

“…This framework has been previously applied to detect bone fractures and tumors in radiography. 3 , 4 However, it has not been applied to detect specific anatomical structures or organs during surgery. The algorithm is an open-source system that can show the existence and range of an object to be detected from an unknown image by examining the visual information.…”

Automatic detection of mesiodens on panoramic radiographs using artificial intelligence

“…Recently, various algorithms such as the YOLO algorithm, deformable parts model, and R-CNN algorithm have been introduced and found to effectively detect cancer, nodules, fractures, or other lesions on medical images 31 – 34 . The YOLO algorithm was first proposed by Redmon et al 24 , and it has been applied in the dental field to detect various diseases on panoramic radiographs 17 , 29 , 35 , 36 . Yang et al 35 detected cysts and tumors of the jaw using YOLOv2 and obtained a precision of 0.707 and recall of 0.680.…”

“…Kwon et al 17 developed a YOLOv3 model that showed high performance, with sensitivity values of 91.4%, 82.8%, 98.4%, and 71.7% for dentigerous cysts, periapical cysts, odontogenic keratocysts, and ameloblastomas, respectively. Son et al 36 developed a model to detect mandibular fractures using YOLOv4.…”

Detection of mandibular fractures on panoramic radiographs using deep learning

“…In oral and maxillofacial surgery, few studies have explored the capability of CNNs to detect mandibular fractures on PR automatically 10 , 21 . However, none of the studies has explored the detection and multi-classification (e.g., condyle, coronoid, ramus, paramedian, median, angle) of mandibular fractures on PR.…”