Tran awarded travel grant to AADOCR
Gwendelyn Tran
Gwendelyn Tran, a second-year dental student at Texas A&M College of Dentistry, was among 37 recipients of travel grants from The American Association for Dental, Oral, and Craniofacial Research. Each grantee was recognized March 25 at the opening ceremony of the association’s 55th annual meeting.
The event ran March 25-March 28 in San Diego, California. It was held in conjunction with the 104th General Session of the International Association for Dental, Oral, and Craniofacial Research and the 50th Annual Meeting of the Canadian Association for Dental Research.
Funding from the grant and from A&M Dentistry allowed Tran to present research on orbital anatomy and the integration of emerging technology in the field. It was the same research that gained her a first-place finish April 1 at A&M Dentistry’s 51st annual Research Scholars Day in an oral presentation. Tran’s mentors are Drs. Andrew Read-Fuller, clinical associate professor of oral maxillofacial surgery and director of the oral and maxillofacial surgical residency program, and Chi Zhang, instructional assistant professor in biomedical sciences.
Zhang’s research inspired her.
“Through my research, I learned that orbital segmentation (the process of digitally labeling orbital structures) is a fundamental component of modern orbital research and technological development,” Tran said. “However, manual segmentation is both time-consuming and unreliable.”
To address this, a deep learning model was trained to automate the segmentation process using augmented data samples.
“The model produced promising results, and in the future, we plan to further retrain and refine it to improve accuracy,” she said. “This research expanded my understanding of the growing role of advanced technologies in the dental industry.”
Zhang praised Tran’s curiosity, persistence and maturity in approaching difficult research problems.
“During her summer research, she quickly learned complex technological concepts and engaged thoughtfully with a technically demanding project on deep learning-based image segmentation for orbital fracture,” Zhang said. “She showed real dedication in learning new methods and working through challenges. The work involved many technical hurdles, especially in software setup and computing, but she remained diligent throughout.”