Development of a Mathematical Model for Balloon Diameter Calculation in Percutaneous Transluminal Angioplasty Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8414

@InProceedings{benolic:2023:SICAAI,

• author = "Leo Benolic",
• title = "Development of a Mathematical Model for Balloon Diameter Calculation in Percutaneous Transluminal Angioplasty Using Genetic Programming",
• booktitle = "Second Serbian International Conference on Applied Artificial Intelligence",
• year = "2023",
• editor = "Nenad Filipovic",
• volume = "999",
• series = "Lecture Notes in Networks and Systems",
• pages = "7--20",
• address = "Kragujevac, Serbia",
• month = may # " 19-20",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-3-031-60840-7",
• URL = "https://link.springer.com/chapter/10.1007/978-3-031-60840-7_2",
• DOI = "doi:10.1007/978-3-031-60840-7_2",
• abstract = "This paper describes the development of a mathematical model using genetic programming to calculate the diameter of a percutaneous transluminal angioplasty (PTA) balloon dilatation catheter for a given pressure and balloon size. The dataset used for the study was provided by Boston Scientific, and the genetic programming algorithm was implemented in Python using parallel computing. The results showed high levels of accuracy, with R2 values of 0.99989 and 0.99954 for the best and parsimonious models, respectively. The developed model can be useful for in-silico simulations of angioplasty surgery and can contribute to improving the effectiveness of the PTA balloon dilatation catheter procedure. This study demonstrates the potential of machine learning techniques for optimizing medical device performance and design. Further work could investigate the use of other machine learning techniques and larger datasets to enhance the accuracy and generalizability of the models.",

}

Genetic Programming entries for Leo Benolic

Citations