Using Video Motion Analysis to Quantify Technical Performance Carly E. Glarner, MD,* Yue-Yung Hu, MD,* Chia-Hsiung Chen, MS, Robert G. Radwin, PhD, Qianqian Zhao, MS, Mark Craven, PhD, Carla Pugh, MD, PhD, Matthew Carty, MD, Caprice C. Greenberg MD, MPH, FACS (*Co-first authors)

Disclosures No relevant conflict of interests

Motion Analysis Tracks detailed metrics Technological Advances Objective and reproducible More specific and precise feedback Relies on sensors on surgeon’s hand or instrument Technological Advances Increased availability of recording devices Marker-less video based motion analysis Developed within Industrial Engineering

Aim To determine if motion analysis using marker-less video-based review could be adapted for the analysis of technical skill performance in the OR

Methods: Case and Patient Selection Index Case: Reduction mammoplasty Attending and resident operate simultaneously Benign breast tissue symmetrical Patients: Women undergoing bilateral reduction mammoplasty Both surgeon and patient consent obtained

Methods: Data Collection 44 Potential cases between 2/29/12-6/22/12 Permission obtained from 6 surgeons 19 Patients consented 11 Cases recorded 9 Cases had in-light camera view 6 Cases yielded usable video for analysis

Methods: Coding Multimedia Video Task Analysis (MVTA) Representative surgical tasks Cutting with electro-cautery (bovie) Cutting with scalpel Suturing Instrument tying

MVTA

Methods: Motion Analysis Spatio-temporal characteristics of the hand movements were extracted Analyzed using an automated, video-based program Marker-less Similar results to traditional sensor-based motion analyses performed in the laboratory setting

Hand Motion Analysis Software

Methods: Statistical Analysis Analysis of displacement, velocity, and acceleration by: Task Surgeon level Descriptive statistics were generated for: Dominant hand Non-dominant hand Difference between hands

Results: Cutting with Bovie

Results: Cutting with Scalpel

Results: Suturing

Results: Instrument Tying p=0.05 p=0.01

Conclusions Video- based motion analysis is a feasible way to collect data on technical performance No markers or other types of sensors Applicable for open operations Future directions Analyze a larger range of surgical abilities and operation types Continue to develop metrics Better characterize acquisition of technical skills Making the analysis process more automated

Thank you Participants Research Team Funding BWH Plastic Surgeons and Residents BWH Patients Research Team Yue-Yung Hu Chia-Hsiung Chen Robert Radwin Qianqian Zhao Mark Craven Carla Pugh Matthew Carty Caprice Greenberg Funding T32 Surgical Oncology Grant David M. Mahvi Research Fellowship Award SUS-KSEA Resident Scholarship