Visual Intelligence: Prediction of Unintentional Surgical-Tool-Induced Bleeding during Robotic and Laparoscopic Surgery

Robotics

Abhilash Pandya¹  Hao Ying¹  Mostafa Daneshgar Rahbar¹ 

[1] Department of Electrical and Computer Engineering, Wayne State University, Detroit, MI 48202, USA;

关键词: minimally invasive surgery;    bleeding prediction;    abrupt tool movement detection;    entropy;    segmentation;    computer vision;   

DOI  :  10.3390/robotics10010037

来源: DOAJ

【 摘 要 】

Unintentional vascular damage can result from a surgical instrument’s abrupt movements during minimally invasive surgery (laparoscopic or robotic). A novel real-time image processing algorithm based on local entropy is proposed that can detect abrupt movements of surgical instruments and predict bleeding occurrence. The uniform nature of the texture of surgical tools is utilized to segment the tools from the background. By comparing changes in entropy over time, the algorithm determines when the surgical instruments are moved abruptly. We tested the algorithm using 17 videos of minimally invasive surgery, 11 of which had tool-induced bleeding. Our preliminary testing shows that the algorithm is 88% accurate and 90% precise in predicting bleeding. The average advance warning time for the 11 videos is 0.662 s, with the standard deviation being 0.427 s. The proposed approach has the potential to eventually lead to a surgical early warning system or even proactively attenuate tool movement (for robotic surgery) to avoid dangerous surgical outcomes.

【 授权许可】

Unknown