A U.S.-based Nigerian scholar and control engineer, Mr Kenechukwu Nwajiaku, alongside his research team, has developed an intelligent robotic control system that bends and twists skeletal fixation plates to fit patients’ jaws or bones precisely.

Nwajiaku disclosed this in a telephone interview with the News Agency of Nigeria (NAN) in Lagos on Saturday.

He said the innovation combined robotics, machine learning, and advanced control technology to shape patient-specific surgical plates accurately, potentially making reconstructive surgery easier and more precise.

The research team—comprising scholars from Case Western Reserve University and The Ohio State University—includes Nwajiaku, Ethan Regal, Tyler Babinec, Yi Jin, Brian Thurston, Glenn Daehn, Changyong Cao, David Dean, Kenneth Loparo, David Hoelzle, and Robert X. Gao.

“The system uses a Gaussian Process-Enhanced Model Predictive Control framework, known as GP-MPC, to predict how a fixation plate will respond while being bent and twisted.

“Reconstructive surgeons use fixation plates to restore damaged facial and jaw bones following trauma, cancer, congenital conditions or surgical treatment.

“These procedures can affect a patient’s appearance and essential functions, including speaking, chewing, breathing and swallowing, making accurate plate shaping important to surgical outcomes,” he said.

Nwajiaku explained that surgeons often shaped the plates manually to match each patient’s anatomy.

However, the process can be difficult and time-consuming because the metal may partially return toward its original form after force is removed, a behavior known as “springback”.

“Our system predicts the plate’s response, compensates for springback and determines the deformation required to produce the desired shape.

“Working in control and automation taught me that machines do not always behave in the real world exactly as mathematical models predict.

“This inspired me to combine advanced control systems with machine learning so that machines can learn from data, account for uncertainty and make more accurate decisions,” he added.

The Nigerian-born engineer noted that the framework combines physics-based modeling with machine learning to improve the robot’s accuracy and consistency.

“Physics helps us understand how the system should behave, while machine learning accounts for what the simplified model may be missing.

“Combining them creates a control system that is better equipped to handle the nonlinear and uncertain behaviour of the material.

“The peer-reviewed study conducted at Case Western Reserve University evaluated the technology through computer simulations and experiments on a physical robotic testbed.

“In combined bending-and-twisting tests, the GP-enhanced system improved deformation accuracy by approximately 22 per cent along the bending axis and 34 per cent along the twisting axis compared with conventional Model Predictive Control.

“The system also recorded low variability during stochastic simulations, suggesting reliable performance under uncertain conditions,” he said.

Nwajiaku pointed out that further development and clinical validation would be required before the technology could be introduced into routine surgical practice.

He, however, noted that the findings demonstrate the potential of artificial intelligence and robotics to support the precise and automated production of patient-specific surgical components.

“I am inspired by the possibility that the same principles used to make industrial machines more intelligent and precise can be applied to challenges that directly affect people’s lives,” he said.

Nwajiaku, whose research interests include artificial intelligence, machine learning, advanced control systems, robotics, digital-twin technologies, and intelligent automation, added that similar technologies could support other medical and industrial processes requiring precise material shaping and reliable decision-making.

“I want my work to contribute to intelligent systems that solve real problems and create benefits beyond a single laboratory or organisation,” Nwajiaku said.

The research points toward a future in which surgical expertise, robotics, physics-based prediction, and machine learning converge to enable safer, more precise, and patient-specific reconstructive procedures.