Folder Design Optimization with Genetic Algorithm for Drug Coated Balloon Folding

Peripheral Artery Disease (PAD) significantly impacts patient life quality by reducing blood flow to the extremities, leading to severe complications. The treatment protocol often includes the use of drug-coated balloons (DCB) during percutaneous transluminal angioplasty, which can reduce the recurrence of arterial narrowing. However, the effectiveness of DCBs is highly dependent on the precision of the balloon folding process prior to deployment. In this paper, we introduce an innovative application of artificial intelligence (AI) to optimize the folding geometry of DCBs. Utilizing a dataset from Boston Scientific, we employ a genetic algorithm (GA) to explore and optimize folder shapes, aiming to enhance the effectiveness and consistency of balloon folding. Our methodology includes defining fitness functions that evaluate the geometrical configuration and operational functionality, ensuring that the folding process minimizes potential damage to the drug coating and maximizes therapeutic outcomes. Results indicate that AI-driven optimization can significantly refine the folding process, offering potential improvements in the clinical application of DCBs. This study does not only advance the engineering of medical devices, but it also illustrates the potential for AI to enhance therapeutic efficacy in the treatment of PAD.