Current Applications of Deep Learning and Radiomics on CT and CBCT for Maxillofacial Diseases
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...
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MDPI AG
2022-12-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/13/1/110 |
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| author | Kuo Feng Hung Qi Yong H. Ai Lun M. Wong Andy Wai Kan Yeung Dion Tik Shun Li Yiu Yan Leung |
| author_facet | Kuo Feng Hung Qi Yong H. Ai Lun M. Wong Andy Wai Kan Yeung Dion Tik Shun Li Yiu Yan Leung |
| author_sort | Kuo Feng Hung |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T10:05:24Z |
| format | Article |
| id | doaj.art-f27e84c52bc54bb79f4462f96510649c |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-11T10:05:24Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-f27e84c52bc54bb79f4462f96510649c2023-11-16T15:08:58ZengMDPI AGDiagnostics2075-44182022-12-0113111010.3390/diagnostics13010110Current Applications of Deep Learning and Radiomics on CT and CBCT for Maxillofacial DiseasesKuo Feng Hung0Qi Yong H. Ai1Lun M. Wong2Andy Wai Kan Yeung3Dion Tik Shun Li4Yiu Yan Leung5Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, ChinaHealth Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, ChinaImaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, ChinaOral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, ChinaOral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, ChinaOral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, ChinaThe 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.https://www.mdpi.com/2075-4418/13/1/110artificial intelligencedeep learningradiomicscomputed tomographycone-beam computed tomographymaxillofacial diseases |
| spellingShingle | Kuo Feng Hung Qi Yong H. Ai Lun M. Wong Andy Wai Kan Yeung Dion Tik Shun Li Yiu Yan Leung Current Applications of Deep Learning and Radiomics on CT and CBCT for Maxillofacial Diseases Diagnostics artificial intelligence deep learning radiomics computed tomography cone-beam computed tomography maxillofacial diseases |
| title | Current Applications of Deep Learning and Radiomics on CT and CBCT for Maxillofacial Diseases |
| title_full | Current Applications of Deep Learning and Radiomics on CT and CBCT for Maxillofacial Diseases |
| title_fullStr | Current Applications of Deep Learning and Radiomics on CT and CBCT for Maxillofacial Diseases |
| title_full_unstemmed | Current Applications of Deep Learning and Radiomics on CT and CBCT for Maxillofacial Diseases |
| title_short | Current Applications of Deep Learning and Radiomics on CT and CBCT for Maxillofacial Diseases |
| title_sort | current applications of deep learning and radiomics on ct and cbct for maxillofacial diseases |
| topic | artificial intelligence deep learning radiomics computed tomography cone-beam computed tomography maxillofacial diseases |
| url | https://www.mdpi.com/2075-4418/13/1/110 |
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