Hagenah, JannisJannisHagenahScharfschwerdt, MichaelMichaelScharfschwerdtSchlaefer, AlexanderAlexanderSchlaeferMetzner, ChristophChristophMetzner2025-08-152025-08-152015-09-12Current directions in biomedical engineering 1 (1): 361–365 (2015)https://hdl.handle.net/11420/56638Abstract Choosing the optimal prosthesis size and shape is a difficult task during surgical valve-sparing aortic root reconstruction. Hence, there is a need for surgery planning tools. Common surgery planning approaches try to model the mechanical behaviour of the aortic valve and its leaflets. However, these approaches suffer from inaccuracies due to unknown biomechanical properties and from a high computational complexity. In this paper, we present a new approach based on machine learning that avoids these problems. The valve geometry is described by geometrical features obtained from ultrasound images. We interpret the surgery planning as a learning problem, in which the features of the healthy valve are predicted from these of the dilated valve using support vector regression (SVR). Our first results indicate that a machine learning based surgery planning can be possible.en2364-550420151De Gruyterhttps://creativecommons.org/licenses/by-nd/3.0/aortic root reconstructiontransesophageal ultrasoundsurgery planningsupport vector regressionmachine learningTechnology::617: Surgery, Regional Medicine, Dentistry, Ophthalmology, Otology, Audiology::617.9: Operative Surgery and Special Fields of SurgeryComputer Science, Information and General Works::006: Special computer methods::006.3: Artificial IntelligenceJournal Article2025-07-30https://doi.org/10.15480/882.1552410.1515/cdbme-2015-008910.15480/882.15524Journal Article