Machine learning–based operation skills assessment with vascular difficulty index for vascular intervention surgery

• Published in: Medical & biological engineering & computing 2020-08, Vol.58 (8), p.1707-1721
• Main Authors: Guo, Shuxiang, Cui, Jinxin, Zhao, Yan, Wang, Yuxin, Ma, Youchun, Gao, Wenyang, Mao, Gengsheng, Hong, Shunming
• Format: Article
• Language: English
• Subjects: Adult; Aorta; Aorta, Thoracic - physiopathology; Aorta, Thoracic - surgery; Aortic arch; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Cardiovascular system; Catheters; Computer Applications; Endovascular Procedures - education; Female; Human Physiology; Humans; Imaging; Learning algorithms; Machine Learning; Male; Medical instruments; Middle Aged; Original Article; Radiology; Robotic surgery; Robotic Surgical Procedures - education; Robots; Skills; Smoothness; Surgeons; Surgeons - education; Surgery; Task Performance and Analysis; Vascular Access Devices; Vascular surgery
• ISSN: 0140-0118; 1741-0444
• DOI: 10.1007/s11517-020-02195-9

An accurate assessment of surgical operation skills is essential for improving the vascular intervention surgical outcome and the performance of endovascular surgery robots. In existing studies, subjective and objective assessments of surgical operation skills use a variety of indicators, such as the operation speed and operation smoothness. However, the vascular conditions of particular patients have not been considered in the assessment, leading to deviations in the evaluation. Therefore, in this paper, an operation skills assessment method including the vascular difficulty level index for catheter insertion at the aortic arch in endovascular surgery is proposed. First, the model describing the difficulty of the vascular anatomical structure is established with characteristics of different aortic arch branches based on machine learning. Afterwards, the vascular difficulty level is set as an objective index combined with operating characteristics extracted from the operations performed by surgeons to evaluate the surgical operation skills at the aortic arch using machine learning. The accuracy of the assessment improves from 86.67 to 96.67% after inclusion of the vascular difficulty as an evaluation indicator to more objectively and accurately evaluate skills. The method described in this paper can be adopted to train novice surgeons in endovascular surgery, and for studies of vascular interventional surgery robots. Graphical abstract Operation skill assessment with vascular difficulty for vascular interventional surgery