The Division of Oral and Maxillofacial Radiology is proud to announce the publication of several groundbreaking articles on the application of deep learning in dental radiology. Our esteemed staff members have contributed their expertise and research findings, revolutionizing the field with innovative approaches and advancements. We invite you to explore these noteworthy publications, which showcase the potential of deep learning in transforming dental diagnostic imaging.

1. Rattanachet P, Wantanajittikul K, Panyarak W, Charoenkwan P, Monum T, Prasitwattanaseree S, Palee P, Mahakkanukrauh P. A Web Application for Sex and Stature Estimation from Radiographic Proximal Femur for A Thai Population. Legal Medicine. 2023 Jun 6:102280.

2. Panyarak W, Suttapak W, Wantanajittikul K, Charuakkra A, Prapayasatok S. Assessment of YOLOv3 for caries detection in bitewing radiographs based on the ICCMS™ radiographic scoring system. Clinical Oral Investigations. 2023 Apr;27(4):1731-42.

3. Panyarak W, Wantanajittikul K, Suttapak W, Charuakkra A, Prapayasatok S. Feasibility of deep learning for dental caries classification in bitewing radiographs based on the ICCMS™ radiographic scoring system. Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology. 2023 Feb 1;135(2):272-81.

4. Upalananda W, Wantanajittikul K, Na Lampang S, Janhom A. Semi-automated technique to assess the developmental stage of mandibular third molars for age estimation. Australian Journal of Forensic Sciences. 2023 Jan 2;55(1):23-33.

5. Charuakkra A, Mahasantipiya P, Lehtinen A, Koivisto J, Järnstedt J. Comparison of subjective image analysis and effective dose between low-dose cone-beam computed tomography machines. Dentomaxillofacial Radiology. 2023 Jan;52(2):20220176.

6. Malatong Y, Palee P, Sinthubua A, Na Lampang S, Mahakkanukrauh P. Estimating age from digital radiographic images of lumbar vertebrae in a Thai population using an image analysis technique. Medicine, Science and the Law. 2022 Jul;62(3):180-7.

7. Suttapak W, Panyarak W, Jira-Apiwattana D, Wantanajittikul K. A unified convolution neural network for dental caries classification. ECTI Transactions on Computer and Information Technology (ECTI-CIT). 2022 Jun 4;16(2):186-95.

These publications highlight the division's commitment to pushing the boundaries of dental radiology and leveraging the potential of deep learning techniques. By harnessing the power of artificial intelligence, our staff members are pioneering new avenues for accurate diagnosis, treatment planning, and patient care in the field of oral and maxillofacial radiology.

We encourage researchers, academicians, and dental professionals to delve into these articles, as they provide valuable insights and contribute to the ever-evolving landscape of deep learning in dental radiology. The Division of Oral and Maxillofacial Radiology is dedicated to fostering collaboration and knowledge exchange, and we are thrilled to share our staff's contributions with the global dental community.

We extend our heartfelt congratulations to the staff members involved and express our gratitude for their significant contributions to advancing dental radiology through deep learning. Together, let us embrace the transformative potential of deep learning in dental radiology and continue to drive innovation for the betterment of oral healthcare.