Daochang Liu1, Tingting Jiang2, Yizhou Wang1,3, Rulin Miao4, Fei Shan4, Ziyu Li4. 1. Department of Computer Science, Peking University, Beijing, China. 2. Department of Computer Science, Peking University, Beijing, China. ttjiang@pku.edu.cn. 3. Deepwise AI Lab, Beijing, China. 4. Peking University Cancer Hospital, Beijing, China.
Abstract
PURPOSE: Automatic surgical skill assessment is an emerging field beneficial to both efficiency and quality of surgical education and practice. Prior works largely evaluate skills on elementary tasks performed in the simulation laboratory, which cannot fully reflect the variety of intraoperative circumstances in the real operating room. In this paper, we attempt to fill this gap by expanding surgical skill assessment onto a clinical dataset including fifty-seven in vivo surgeries. METHODS: To tackle the workflow and device constraints in the clinical setting, we propose a robust and non-interruptive surrogate for surgical skills, namely the clearness of operating field (COF), which shows strong correlation with overall skills and high inter-annotator consistency on our clinical data. Then, an automatic model based on neural networks is developed to regress surgical skills through the surrogate of COF using only video as input. RESULTS: The automatic model achieves 0.595 Spearman's correlation with the ground truth of overall technical skill, which even exceeds the human performance of junior surgeons. Moreover, an exploratory study is conducted to validate the skill predictions against the clinical outcomes of patients. CONCLUSION: Our results demonstrate that the surrogate of COF is promising and the approach is potentially applicable to clinical practice.
PURPOSE: Automatic surgical skill assessment is an emerging field beneficial to both efficiency and quality of surgical education and practice. Prior works largely evaluate skills on elementary tasks performed in the simulation laboratory, which cannot fully reflect the variety of intraoperative circumstances in the real operating room. In this paper, we attempt to fill this gap by expanding surgical skill assessment onto a clinical dataset including fifty-seven in vivo surgeries. METHODS: To tackle the workflow and device constraints in the clinical setting, we propose a robust and non-interruptive surrogate for surgical skills, namely the clearness of operating field (COF), which shows strong correlation with overall skills and high inter-annotator consistency on our clinical data. Then, an automatic model based on neural networks is developed to regress surgical skills through the surrogate of COF using only video as input. RESULTS: The automatic model achieves 0.595 Spearman's correlation with the ground truth of overall technical skill, which even exceeds the human performance of junior surgeons. Moreover, an exploratory study is conducted to validate the skill predictions against the clinical outcomes of patients. CONCLUSION: Our results demonstrate that the surrogate of COF is promising and the approach is potentially applicable to clinical practice.
Authors: Lee Richstone; Michael J Schwartz; Casey Seideman; Jeffrey Cadeddu; Sandra Marshall; Louis R Kavoussi Journal: Ann Surg Date: 2010-07 Impact factor: 12.969
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