Researchers at Washington State University (WSU) unveiled a new AI-guided 3D printing technique that can help physicians print intricate replicas of human organs. Surgeons can then use these organ models to practice before performing the actual surgery, which gives doctors more tools to improve surgical results.
The AI algorithm was trained on images and key attributes of human kidneys and prostates, including characteristics like weight, size, porosity, and vascular architecture. The algorithm works with 3D printers in a process of improvement. It helps find the best settings for three important parts of 3D printing: how accurate the model is, how light it is, and how long it takes to print.
One of the co-authors of the study, Kaiyan Qiu, an assistant professor of mechanical and materials engineering at WSU, said that AI optimizations can materially shorten the time it takes to create viable 3D models. The algorithm adjusts key 3D printing variables, including a printer’s nozzle size and travel speed, the pressure printing materials are dispensed at, and the height of each printed layer.. It then guides the printer in creating an appropriate model for a specific use-case.
“For pre-surgical organ models, we know surgeons will need high fidelity models that can be printed out quickly and with low labor intensity,” Prof. Qiu said. “We imagine a scenario where a surgeon receives an MRI and CT scan [of a patient] in the morning. She has two hours to prepare everything for surgery. The AI can optimize the parameters, and print out a model organ in half-an-hour, and the surgeon can then spend the remaining time practicing [on the organ replica].”
Qiu and his co-author on the paper, WSU computer science professor, Jana Doppa, used a multi-objective Bayesian Optimization (BO) approach using BoTorch to improve the efficiency and precision of the 3D printing process. The BO algorithm uses a probabilistic surrogate model to approximate the relationship between printing parameters and the quality of the printed organ models. This process captures uncertainties in the printing process, allowing for more robust optimization.
The researchers used NVIDIA A40 GPUs to train their AI model, and NVIDIA NGP Instant NeRF to reconstruct a mesh object of the 3D printed model.
The AI process the researchers have pioneered is also broadly generalizable. Besides printing model organs, the algorithm can guide printers to create prototypes of implantable medical devices, like pacemakers or stents. The technology can also be used to make models of airplane and robot parts, batteries, or even shoes customized for you.
To learn more about this research, you can read this article, or this research paper.
Featured image credit: Washington State University