Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/105302
Title: | Current applications of deep learning and radiomics on CT and CBCT for maxillofacial diseases | Authors: | Hung, KF Ai, QYH Wong, LM Yeung, AWK Li, DTS Leung, YY |
Issue Date: | Jan-2023 | Source: | Diagnostics, Jan. 2023, v. 13, no. 1, 110 | 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. | Keywords: | Artificial intelligence Computed tomography Cone-beam computed tomography Deep learning Maxillofacial diseases Radiomics |
Publisher: | MDPI AG | Journal: | Diagnostics | EISSN: | 2075-4418 | DOI: | 10.3390/diagnostics13010110 | Rights: | © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). The following publication Hung KF, Ai QYH, Wong LM, Yeung AWK, Li DTS, Leung YY. Current Applications of Deep Learning and Radiomics on CT and CBCT for Maxillofacial Diseases. Diagnostics. 2023; 13(1):110 is available at https://doi.org/10.3390/diagnostics13010110. |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
diagnostics-13-00110-v2.pdf | 1.47 MB | Adobe PDF | View/Open |
Page views
11
Citations as of Jul 7, 2024
Downloads
2
Citations as of Jul 7, 2024
SCOPUSTM
Citations
15
Citations as of Jul 4, 2024
WEB OF SCIENCETM
Citations
16
Citations as of Jul 4, 2024
![](/image/google_scholar.jpg)
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.