Michael Brockman^1,, Jarren Adam², Cassandra De La Torre³, Lucas Achkarian⁴, Robert Hanna⁵

Purpose: Currently, the femoral artery is the most common vascular access site for interventional procedures. With medical instrument advancements and improved procedure techniques, the radial artery is becoming the preferred vascular access point for interventionalists due to decreased complication risks and improved recovery times. Although transradial procedures have improved clinical outcomes, additional, pre-interventional imaging is often required to assess the capacity of the smaller radial artery when comparing the femoral artery. This study explores an alternative, machine learning-based method to predict radial artery adequacy for interventional procedures without the need for additional, pre-intervention imaging. Materials and Methods: Whole human body computed tomography (CT) scans were collected from New Mexico’s Decedent Image Database. The inclusion criteria were decedents between the ages of 18 to 99, who died of natural causes, and had no identifiable decomposition. The decedent’s femoral artery diameter, sex assigned at birth, age, carcinogen exposure, smoking history, drinking history, substance usage, height, and weight were recorded. Using Horos Project DICOM viewer, the right femoral and right radial artery of each CT scan was measured. The resulting measurements were then passed through the Python 3.9 module, Scikit-Learn, to create a predictive random forest classifier. Results: Femoral and radial artery diameters were measured on 127 decadents. The predetermined threshold for radial artery usability diameter was 2.5 mm, which was based on guidance from Hobby et al’s publication, Transradial Access: A Comprehensive Review. The performed linear regression of femoral artery diameter explains 54.9% of the variability found in radial artery diameter. After one-thousand training iterations, the random forest classifier model achieved a mean testing accuracy of 85.71% in predicting if the radial artery equal to or greater than 2.5mm.

the presented study preliminarily suggests that femoral artery diameter and other parameters, such as age, height, weight, and smoking history, can be used for training a random forest classifier model to assess radial artery diameter