FDT-biotech: trustworthy digital twins for patient-specific evaluation of implant systems

The National Science Foundation Division of Mathematical Sciences awarded Texas A&M Engineering Experiment Station $513,000 on June 1, 2027, under the Mathematical and Physical Sciences program (CFDA 47.049) to develop the scientific foundation for patient-specific digital twins that compare candidate all-on-4 dental implant configurations and quantify confidence in each comparison.

The project creates computational methods to evaluate how implant position and angle affect chewing forces on the jawbone, with the core innovation of distinguishing well-supported differences from uncertain cases rather than forcing a choice when evidence is weak. The system will explicitly flag when evidence is limited for underrepresented patient populations or uses outside tested conditions, reducing risk of overconfident or inequitable conclusions. Beyond dentistry, the same foundation could support evaluation of other implants and medical technologies where direct human comparison is impractical or unsafe. The project will also prepare graduate and undergraduate students in mathematics, engineering, biomedical data analysis, and responsible decision-making in health technology development.

Performance is located in College Station, Texas, with a period of performance from June 1, 2027, through May 31, 2030.

Abstract

Complete tooth loss can severely impair oral function and day-to-day quality of life. All-on-4 treatment uses four dental implants to support a fixed full set of replacement teeth in one jaw. Different implant positions and angles can change how chewing forces reach the jawbone, but several alternatives cannot be tested in the same person. Computer models could help compare options before later-stage testing, yet their conclusions can change when patient anatomy, bone behavior, the implant-bone connection, or loading assumptions change. This project will create the scientific foundation for a patient-specific digital twin, a computer-based representation of a patient and treatment, that compares candidate all-on-4 configurations and states how much confidence should be placed in each comparison. Its main innovation is that IT will not force a choice when evidence is weak; IT will distinguish well-supported differences from cases that remain uncertain. IT will also make clear when evidence is limited for underrepresented cases or uses outside tested conditions, reducing the risk of overconfident or inequitable conclusions. This capability could make early medical-device evaluation safer, faster, and more transparent by replacing isolated simulations and unsupported assumptions with a systematic comparison process that reports uncertainty and model limits. Beyond dentistry, the same foundation could support evaluation of other implants and medical technologies when direct human comparison is risky or impractical. The project will also prepare graduate and undergraduate students to combine mathematics, engineering, biomedical data, and responsible decision-making in the development of future health technologies. The specific research problem is how to determine whether comparative in silico evidence among candidate all-on-4 configurations is sufficiently credible for preclinical medical-device evaluation when patient anatomy, material properties, implant-bone interaction, and loading are uncertain. Mandibular all-on-4 implant-prosthesis systems provide the testbed: candidate configurations vary in implant position, angulation, anterior-posterior spread, and local bone support, while uncertainty in unobserved peri-implant young's modulus and poisson's ratio can alter predicted strain, overload burden, and apparent separation among configurations. The research will establish mathematical, statistical, and engineering foundations through four linked components. First, probabilistic inverse elasticity will recover heterogeneous peri-implant young's modulus and poisson's ratio from controlled implant-bone loading, computed tomography (CT), and three-dimensional displacement fields. Second, a calibrated hierarchical model will personalize the resulting elastic-property distributions and quantify their uncertainty for new cases using preoperative CT and clinical factors, incorporating a literature-supported density-modulus relationship for young's modulus. Third, a hybrid physics-informed surrogate will map patient anatomy, all-on-4 configuration variables, loading conditions, and elastic-property posterior to displacement, strain, and overload-burden outputs. Finally, verification, validation, uncertainty calibration, and robustness criteria will determine when a configuration preference is credible and when the comparison must remain unresolved. The resulting advance will be a transferable methodology for uncertainty-bounded comparative evaluation of biomedical device configurations when direct human comparison is impractical. This project is jointly supported by the national institutes of health through the office of data science strategy and the national institute of dental and craniofacial research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the foundation's intellectual merit and broader impacts review criteria.- subawards are not planned for this award.