Các nhà nghiên cứu NUS kết hợp công nghệ in sinh học 3D với AI để cá nhân hóa các mô ghép mềm trong miệng 

Artificial intelligence streamlines the optimisation of bioprinting parameters for oral soft tissue grafts, reducing time and resource demands while enabling efficient, personalised dental treatments

Assistant Professor Gopu Sriram (left) and Dr Jacob Chew (middle) from NUS Faculty of Dentistry, together with Professor Dean Ho (right) from the Department of Biomedical Engineering in the College of Design and Engineering at NUS, showcasing their groundbreaking work on AI-driven 3D bioprinting for personalised gingival tissue grafts. The team’s novel approach seeks to make dental procedures less invasive while enhancing patient outcomes.

A team of researchers from the National University of Singapore (NUS) has developed a method to fabricate personalised gingival (gum) tissue grafts using an innovative combination of 3D bioprinting and artificial intelligence (AI).

Led by Assistant Professor Gopu Sriram from NUS Faculty of Dentistry, the team’s approach presents a more customisable and less invasive alternative to traditional grafting methods, which often involve harvesting tissue from the patient’s mouth — a process that can be both uncomfortable and constrained by the availability of suitable tissue.

The 3D bioprinting and AI-enabled technique has the potential to address key challenges in dental procedures more effectively, such as repairing gum defects caused by periodontal disease or complications from dental implants. For instance, by enabling the precise fabrication of tissue constructs tailored to individual patients, the method can significantly improve treatment outcomes, reduce patient discomfort, and minimise the risk of complications, such as infections, during recovery.

The team’s research was published in the journal Advanced Healthcare Materials on 17 December 2024, and was supported by grants from National Additive Manufacturing Innovation Cluster (NAMIC) and National University Health System (NUHS).

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