Current Applications of Deep Learning and Radiomics on CT and CBCT for Maxillofacial Diseases

Abstract

The increasing use of computed tomography (CT) and cone beam computed tomography (CBCT) in oral and maxillofacial imaging has driven the development of deep learning and radiomics applications to assist clinicians in early diagnosis, accurate prognosis prediction, and efficient treatment planning of maxillofacial diseases. This narrative review aimed to provide an up-to-date overview of the current applications of deep learning and radiomics on CT and CBCT for the diagnosis and management of maxillofacial diseases. Based on current evidence, a wide range of deep learning models on CT/CBCT images have been developed for automatic diagnosis, segmentation, and classification of jaw cysts and tumors, cervical lymph node metastasis, salivary gland diseases, temporomandibular (TMJ) disorders, maxillary sinus pathologies, mandibular fractures, and dentomaxillofacial deformities, while CT-/CBCT-derived radiomics applications mainly focused on occult lymph node metastasis in patients with oral cancer, malignant salivary gland tumors, and TMJ osteoarthritis. Most of these models showed high performance, and some of them even outperformed human experts. The models with performance on par with human experts have the potential to serve as clinically practicable tools to achieve the earliest possible diagnosis and treatment, leading to a more precise and personalized approach for the management of maxillofacial diseases. Challenges and issues, including the lack of the generalizability and explainability of deep learning models and the uncertainty in the reproducibility and stability of radiomic features, should be overcome to gain the trust of patients, providers, and healthcare organizers for daily clinical use of these models.

Radiographic Imaging for the Diagnosis and Treatment of Patients with Skeletal Class III Malocclusion

“…Several ML models have been developed in orthodontics to identify relevant variables with greater accuracy compared to conventional statistical models [ 17 , 80 , 81 , 82 ]. Recently, two studies have developed deep learning models to predict the need for orthognathic surgery using lateral cephalograms [ 47 , 83 ].…”

“…Recently, two studies have developed deep learning models to predict the need for orthognathic surgery using lateral cephalograms [ 47 , 83 ]. Although these deep learning models obtained favorable prediction accuracy, interpreting their prediction outcomes is challenging due to their “black box” nature, making the decision-making process difficult to explain [ 17 ]. Most recently, it has been raised that quantitative textural imaging analysis could be utilized to predict the growth of specific organs or the progression of lesions [ 84 , 85 ].…”

“…Radiomics is an approach enabling the extraction of a large number of quantitative features in medical images and providing a detailed characterization of the underlying tissue properties [ 15 ]. It has been explored in the automatic diagnosis and prognosis prediction of maxillofacial diseases, particularly in detecting head and neck tumors [ 16 , 17 ].…”

Dental Caries Detection and Classification in CBCT Images Using Deep Learning

“…Convolutional neural networks (CNNs) are a type of deep learning model based on neural networks in artificial intelligence that are used for image classification and object detection in images. 10 , 11 , 12 , 13 CNNs can detect dental caries and analyse dental images to identify caries regions. 14 They can identify dental caries signs in images, enabling early caries detection using trained neural networks.…”

Evaluating a Periapical Lesion Detection CNN on a Clinically Representative CBCT Dataset—A Validation Study

“…Specifically, these techniques have been applied to panoramic radiographs and cone-beam computed tomography (CBCT) images with the aim of assisting clinicians in detecting and analyzing dental conditions and diseases in the maxillofacial region [ 9 , 10 , 11 ]. Examples include the detection of maxillary sinus mucosa [ 12 ], pharyngeal airway space [ 13 ], calcifications of the cervical carotid artery [ 14 ], jaw cysts [ 15 , 16 ], supernumerary mesio-buccal root canals on maxillary molars [ 17 ], vertical root fractures [ 18 ], and periapical lesions (PALs).…”