Robotic excision of aortic valve papillary fibroelastoma and concomitant maze procedure

Comparative Analysis of Machine Learning Techniques for Identifying Multiple Force Systems from Accelerometer Measurements

The knowledge of the forces acting on a structure enables, among many other factors, assessments of whether the component's useful life is compromised by the current machine condition. In many cases, a direct measurement of those forces becomes unfeasible, and an inverse problem must be solved. Among the solutions developed, machine learning techniques have stood out as powerful predictive tools increasingly applied to engineering problem-solving. This study evaluates the ability of different machine learning models to identify parameters of multi-force systems from accelerometer measurements. The models were assessed according to their prediction potential based on correlation coefficient (R2), mean relative error (MRE), and processing time. A computational numerical model using the finite element method was generated and validated by vibration measurements performed using accelerometers in the laboratory. A robust database created by the response surface methodology in conjunction with Design of Experiment (DOE) was used for the evaluation of the ability of machine learning models to predict the position, frequency, magnitude, and number of forces acting on a structure. Among the six machine learning models evaluated, k-NN was able to predict with a 0.013% error, and Random Forests showed a maximum error of 0.2%. The innovation of this study lies in the application of the proposed method for identifying parameters of multi-force systems.

A Case Series of Minimally Invasive Robotic-Assisted Resection of Cardiac Papillary Fibroelastoma: The Mayo Clinic Experience

Papillary fibroelastomas (PFEs) are small, slowly growing benign cardiac tumors with clinically significant risk of embolization. Surgical excision is the definitive treatment of symptomatic PFE and is conventionally performed through a median sternotomy. In this study, we report a series of 12 patients, who underwent robotic-assisted PFE removal at the Mayo Clinic. PFE involved the mitral valve, left atrium, and tricuspid valve. No major complications occurred after the procedure, and most patients were discharged 4 days after the surgery. On follow-up, 1 patient demonstrated pericarditis.