An objective and automated method for assessing surgical skill in endoscopic sinus surgery using eye‐tracking and tool‐motion data

Abstract

We present an objective and unbiased method for assessing the skill of endoscopic sinus surgeons. Experimental results show that the proposed method successfully identifies the skill levels of both expert and novice surgeons.

Eye-Tracking in the Study of Visual Expertise: Methodology and Approaches in Medicine

“…These studies often utilize static images, which are relatively easy to employ in eye-tracking experimental designs and analyses, but which don't always translate to the visual environments of other clinical specialties. In recent years, additional studies have appeared in relation to fields such as dermatology, surgery, and anatomic pathology (Ahmidi, Ishii, Fichtinger, Gallia, & Hager, 2012;Bombari, Mora, Schaefer, Mast, & Lehr, 2012;Brunye et al, 2014;Fox, Law, & Faulkner-Jones, 2017;E. Krupinski, Chao, Hofmann-Wellenhof, Morrison, & Curiel-Lewandrowski, 2014;E.…”

Operating room of the future

“…87 More recently, combining eye tracking with tool-motion tracking seems to offer a feasible method of assessing skill level. 88,89 The pinnacle of clinical application of eye tracking will be error prediction; such a strategy will in essence be one application of artificial intelligence in the ORF. Surgeon-and procedure-specific data will be gathered from numerous performances that can allow a simulation model to predict an operative pathway.…”

Objective Assessment of Surgical Technical Skill and Competency in the Operating Room

“…Several different clustering techniques have been used for OCASE-T, including vector quantization (VQ) with algorithms such as K-means, piecewise linear approximation (PLA) (66), and DCC (57). K-means was used to discretize raw time-series data from sensors (56, 58, 60, 62, 72, 87, 90, 91, 95) and to cluster coefficients from STFT (76). Other techniques, such as symbolic aggregation approximation (SAX), aligned cluster analysis (ACA), Persist (130), and methods based on affine velocities and the two-thirds power law (131), have been explored to transform time-series data for surgical activity detection but not for OCASE-T.…”

“…HMMs yielded only low to moderate accuracy (29% to 71%) for skill classification for nasal septoplasty in the operating room (expert versus novice), using tool position, orientation, and velocities (54). In a cadaver model, higher sensitivity in classifying skill was reported when surgeons’ eye-gaze positions were used in addition to position and orientation of surgical tools (83% versus 73%) (56, 58). Although several studies used discrete representations of tool motion and other data such as manual annotations of tool–tissue interactions, whether such discretization translates into improved performance of time-series methods such as MMs or HMMs is unknown (Table 3).…”