Eye Tracking and Motion Data Predict Endoscopic Sinus Surgery Skill

Expert-Novice Level Classification Using Graph Convolutional Network Introducing Confidence-Aware Node-Level Attention Mechanism

In this study, we propose a classification method of expert-novice levels using a graph convolutional network (GCN) with a confidence-aware node-level attention mechanism. In classification using an attention mechanism, highlighted features may not be significant for accurate classification, thereby degrading classification performance. To address this issue, the proposed method introduces a confidence-aware node-level attention mechanism into a spatiotemporal attention GCN (STA-GCN) for the classification of expert-novice levels. Consequently, our method can contrast the attention value of each node on the basis of the confidence measure of the classification, which solves the problem of classification approaches using attention mechanisms and realizes accurate classification. Furthermore, because the expert-novice levels have ordinalities, using a classification model that considers ordinalities improves the classification performance. The proposed method involves a model that minimizes a loss function that considers the ordinalities of classes to be classified. By implementing the above approaches, the expert-novice level classification performance is improved.

Eye-tracking in surgery: a systematic review

BACKGROUND

Surgery is constantly evolving with the assistance of rapidly developing novel technology. Eye-tracking devices provide opportunities to monitor the acquisition of surgical skills, gain insight into performance, and enhance surgical practice. The aim of this review was to consolidate the available evidence for the use of eye-tracking in the surgical disciplines.

METHODS

A systematic literature review was conducted in accordance with PRISMA guidelines. A search of OVID Medline, EMBASE, Cochrane library, Scopus, and Science Direct was conducted January 2000 until December 2022. Studies involving eye-tracking in surgical training, assessment and technical innovation were included in the review. Non-surgical procedures, animal studies, and studies not involving surgical participants were excluded from the review.

RESULTS

The search returned a total of 12 054 articles, 80 of which were included in the final analysis and review. Seventeen studies involved eye-tracking in surgical training, 48 surgical assessment, and 20 were focussing on technical aspects of this technology. Twenty-six different eye-tracking devices were used in the included studies. Metrics such as the number of fixations, duration of fixations, dwell time, and cognitive workload were able to differentiate between novice and expert performance. Eight studies demonstrated the effectiveness of gaze-training for improving surgical skill.

CONCLUSION

The current literature shows a broad range of utility for a variety of eye-tracking devices in surgery. There remains a lack of standardization for metric parameters and gaze analysis techniques. Further research is required to validate its use to establish reliability and create uniform practices.

Eye gaze metrics for skill assessment and feedback in kidney stone surgery

PURPOSE

Surgical skill assessment is essential for safe operations. In endoscopic kidney stone surgery, surgeons must perform a highly skill-dependent mental mapping from the pre-operative scan to the intraoperative endoscope image. Poor mental mapping can lead to incomplete exploration of the kidney and high reoperation rates. Yet there are few objective ways to evaluate competency. We propose to use unobtrusive eye-gaze measurements in the task space to evaluate skill and provide feedback.

METHODS

We capture the surgeons' eye gaze on the surgical monitor with the Microsoft Hololens 2. To enable stable and accurate gaze detection, we develop a calibration algorithm to refine the eye tracking of the Hololens. In addition, we use a QR code to locate the eye gaze on the surgical monitor. We then run a user study with three expert and three novice surgeons. Each surgeon is tasked to locate three needles representing kidney stones in three different kidney phantoms.

RESULTS

We find that experts have more focused gaze patterns. They complete the task faster, have smaller total gaze area, and the gaze fewer times outside the area of interest. While fixation to non-fixation ratio did not show significant difference in our findings, tracking the ratio over time shows different patterns between novices and experts.

CONCLUSION

We show that a non-negligible difference holds between novice and expert surgeons' gaze metrics in kidney stone identification in phantoms. Expert surgeons demonstrate more targeted gaze throughout a trial, indicating their higher level of proficiency. To improve the skill acquisition process for novice surgeons, we suggest providing sub-task specific feedback. This approach presents an objective and non-invasive method to assess surgical competence.

Craniotomy Simulator with Force Myography and Machine Learning-Based Skills Assessment

Craniotomy is a fundamental component of neurosurgery that involves the removal of the skull bone flap. Simulation-based training of craniotomy is an efficient method to develop competent skills outside the operating room. Traditionally, an expert surgeon evaluates the surgical skills using rating scales, but this method is subjective, time-consuming, and tedious. Accordingly, the objective of the present study was to develop an anatomically accurate craniotomy simulator with realistic haptic feedback and objective evaluation of surgical skills. A CT scan segmentation-based craniotomy simulator with two bone flaps for drilling task was developed using 3D printed bone matrix material. Force myography (FMG) and machine learning were used to automatically evaluate the surgical skills. Twenty-two neurosurgeons participated in this study, including novices (n = 8), intermediates (n = 8), and experts (n = 6), and they performed the defined drilling experiments. They provided feedback on the effectiveness of the simulator using a Likert scale questionnaire on a scale ranging from 1 to 10. The data acquired from the FMG band was used to classify the surgical expertise into novice, intermediate and expert categories. The study employed naïve Bayes, linear discriminant (LDA), support vector machine (SVM), and decision tree (DT) classifiers with leave one out cross-validation. The neurosurgeons' feedback indicates that the developed simulator was found to be an effective tool to hone drilling skills. In addition, the bone matrix material provided good value in terms of haptic feedback (average score 7.1). For FMG-data-based skills evaluation, we achieved maximum accuracy using the naïve Bayes classifier (90.0 ± 14.8%). DT had a classification accuracy of 86.22 ± 20.8%, LDA had an accuracy of 81.9 ± 23.6%, and SVM had an accuracy of 76.7 ± 32.9%. The findings of this study indicate that materials with comparable biomechanical properties to those of real tissues are more effective for surgical simulation. In addition, force myography and machine learning provide objective and automated assessment of surgical drilling skills.