Aneeq Zia1, Liheng Guo2, Linlin Zhou2, Irfan Essa3, Anthony Jarc2. 1. College of Computing, Georgia Institute of Technology, North Ave NW, Atlanta, GA, 30332, USA. aneeqzia@gmail.com. 2. Medical Research, Intuitive Surgical, Inc., 5655 Spalding Drive, Norcross, GA, 30092, USA. 3. College of Computing, Georgia Institute of Technology, North Ave NW, Atlanta, GA, 30332, USA.
Abstract
PURPOSE: Surgical task-based metrics (rather than entire procedure metrics) can be used to improve surgeon training and, ultimately, patient care through focused training interventions. Machine learning models to automatically recognize individual tasks or activities are needed to overcome the otherwise manual effort of video review. Traditionally, these models have been evaluated using frame-level accuracy. Here, we propose evaluating surgical activity recognition models by their effect on task-based efficiency metrics. In this way, we can determine when models have achieved adequate performance for providing surgeon feedback via metrics from individual tasks. METHODS: We propose a new CNN-LSTM model, RP-Net-V2, to recognize the 12 steps of robotic-assisted radical prostatectomies (RARP). We evaluated our model both in terms of conventional methods (e.g., Jaccard Index, task boundary accuracy) as well as novel ways, such as the accuracy of efficiency metrics computed from instrument movements and system events. RESULTS: Our proposed model achieves a Jaccard Index of 0.85 thereby outperforming previous models on RARP. Additionally, we show that metrics computed from tasks automatically identified using RP-Net-V2 correlate well with metrics from tasks labeled by clinical experts. CONCLUSION: We demonstrate that metrics-based evaluation of surgical activity recognition models is a viable approach to determine when models can be used to quantify surgical efficiencies. We believe this approach and our results illustrate the potential for fully automated, postoperative efficiency reports.
PURPOSE: Surgical task-based metrics (rather than entire procedure metrics) can be used to improve surgeon training and, ultimately, patient care through focused training interventions. Machine learning models to automatically recognize individual tasks or activities are needed to overcome the otherwise manual effort of video review. Traditionally, these models have been evaluated using frame-level accuracy. Here, we propose evaluating surgical activity recognition models by their effect on task-based efficiency metrics. In this way, we can determine when models have achieved adequate performance for providing surgeon feedback via metrics from individual tasks. METHODS: We propose a new CNN-LSTM model, RP-Net-V2, to recognize the 12 steps of robotic-assisted radical prostatectomies (RARP). We evaluated our model both in terms of conventional methods (e.g., Jaccard Index, task boundary accuracy) as well as novel ways, such as the accuracy of efficiency metrics computed from instrument movements and system events. RESULTS: Our proposed model achieves a Jaccard Index of 0.85 thereby outperforming previous models on RARP. Additionally, we show that metrics computed from tasks automatically identified using RP-Net-V2 correlate well with metrics from tasks labeled by clinical experts. CONCLUSION: We demonstrate that metrics-based evaluation of surgical activity recognition models is a viable approach to determine when models can be used to quantify surgical efficiencies. We believe this approach and our results illustrate the potential for fully automated, postoperative efficiency reports.
Authors: John D Birkmeyer; Jonathan F Finks; Amanda O'Reilly; Mary Oerline; Arthur M Carlin; Andre R Nunn; Justin Dimick; Mousumi Banerjee; Nancy J O Birkmeyer Journal: N Engl J Med Date: 2013-10-10 Impact factor: 91.245
Authors: Andru P Twinanda; Sherif Shehata; Didier Mutter; Jacques Marescaux; Michel de Mathelin; Nicolas Padoy Journal: IEEE Trans Med Imaging Date: 2016-07-22 Impact factor: 10.048
Authors: Andrew J Hung; Jian Chen; Zhengping Che; Tanachat Nilanon; Anthony Jarc; Micha Titus; Paul J Oh; Inderbir S Gill; Yan Liu Journal: J Endourol Date: 2018-03-20 Impact factor: 2.942
Authors: I-Hsuan Alan Chen; Ahmed Ghazi; Ashwin Sridhar; Danail Stoyanov; Mark Slack; John D Kelly; Justin W Collins Journal: World J Urol Date: 2020-11-06 Impact factor: 4.226