AI-assisted catheter design reduces the risk of bacterial infections


Bacteria entering the body through catheters cause one of the most frequent bacterial infections in a medical setting. Although catheters are meant to drain a patient’s fluids, bacteria can use a special swimming motion to drive themselves upstream through catheter tubes and into the body. This results in $300 million in catheter-associated urinary infections each year in the United States.

The solution to the problem:

To resolve this problem, an interdisciplinary effort at Caltech has produced a novel type of catheter tube that stops germs from moving upstream. This helped to negate the need for antibiotics or other chemical antimicrobial treatments. Cutting-edge artificial intelligence (AI) technology optimized the new design, which was less likely to allow germs to swim upstream in lab experiments.

Following the suggestions of Chaira’s research, the team created tubes with shark-fin-like triangular protrusions running the length of the inner wall. That redirected bacterial movement and propelled them back downstream.

Zhou and his team sought to verify the design, requiring additional biology expertise experimentally. So they contacted Olivia Xuan Wan, a postdoctoral scholar at Sternberg Laboratory.

The co-first author of the newspaper, Wan, said:

I study nematode navigation, and this project resonated deeply with my specialized interest in motion trajectories

The team swiftly transitioned from theoretical modelling to practical experimentation. It employed 3D-printed catheter tubes and high-speed cameras to track bacterial progress.

The team conducted simulations to find the most effective triangular obstacle shape to impede bacteria’s upstream swimming. They fabricated microfluidic channels with optimized designs and observed E. coli movement under different flow conditions, confirming the simulated predictions.

The researchers collaborated to enhance the geometric catheter tube design. Researchers collaborated with Anandkumar laboratory AI experts to enhance geometric tube design using advanced neural operators.

The technology accelerated catheter design optimization computations, reducing days to minutes. The model proposed tweaks to geometric design. That enhances the effectiveness of initial triangular shapes by 5% in simulations, preventing bacteria from swimming upstream.

Zhou said:

Our journey from theory to simulation, experiment, and, finally, to real-time monitoring within these microfluidic landscapes is a compelling demonstration of how theoretical concepts can be brought to life, offering tangible solutions to real-world challenges


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