Paper 13408-27
Statistical surgical process modeling of performance and workflow in bronchoscopy
19 February 2025 • 11:10 AM - 11:30 AM PST | Town & Country D
Abstract
A computational framework for statistical surgical process modeling (SPM) was used to quantitatively evaluate performance in transbronchial lung nodule biopsy under fluoroscopy + EBUS compared to 3D cone-beam CT (CBCT) guidance and robotic assistance. Clinical outcomes were measured and validated in terms of procedure cycle time, radiation dose, and diagnostic yield, demonstrating quantitative gains in diagnostic yield for 3D guidance and helping to identify optimal workflow of these emerging technologies.
Presenter
Tatiana Rypinski
The Univ. of Texas M.D. Anderson Cancer Ctr. (United States)
Ms. Tatiana Rypinski is a Data Scientist in the Department of Imaging Physics at The University of Texas MD Anderson Cancer Center and the Surgical Data Science Program in the Institute of Data Science in Oncology. She completed her bachelor’s degree in Biomedical Engineering at Johns Hopkins University in 2015 and a clinical master’s degree in Prosthetics and Orthotics at Baylor College of Medicine in 2021. She also has 5 years experience working in the medical device industry where she supported product, process, and quality changes in the EMS business unit at Stryker Medical and helped scale service operations for a startup developing a robotic transcranial doppler ultrasound product. As a “Surgineer” at UT MD Anderson, she works closely with surgeons and bronchoscopists on the translation of emerging technologies and data science approaches to improve the precision, quality, safety, and value of medical interventions.