Mp26-09 eye Movement Behavior Associates With Expertise Level in Robot-Assisted Surgery

Toussi, Mehdi Seilanian; Shadpour, Saeed; Attwood, Kristopher; Liu, Qian; Gutierrez, Camille; Mohler, james L.; Shafiei, Somayeh B.

doi:10.1097/ju.0000000000003254.09

Journal of Urology

2023

DOI: 10.1097/ju.0000000000003254.09

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Mp26-09 eye Movement Behavior Associates With Expertise Level in Robot-Assisted Surgery

Mehdi Seilanian Toussi¹,

Saeed Shadpour²,

Kristopher Attwood³

et al.

Abstract: development, 14 cases were used for internal validation, and 23 were used as a separate testing cohort for evaluating algorithm accuracy. Concordance between AI-enabled automated video analysis and manual human video annotation was 87.6%. Algorithm accuracy was highest for the vesicourethral anastomosis step (98.6%) and lowest for the final inspection and extraction step (63.0%).CONCLUSIONS: We present results of an AI-enabled computer vision algorithm for automated annotation of full-length RARP surgical vide… Show more

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“…Expert laparoscopic surgeons exhibit shorter fixations and longer saccades compared to novices, indicating more efficient visual search and decision-making [ 10 ]. Eye tracking was suggested as a potential surgical skill evaluation tool [ 10 , 11 ].…”

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confidence: 99%

Performance and learning rate prediction models development in FLS and RAS surgical tasks using electroencephalogram and eye gaze data and machine learning

Shafiei,

Shadpour,

Intes

et al. 2023

Surg Endosc

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Objective This study explored the use of electroencephalogram (EEG) and eye gaze features, experience-related features, and machine learning to evaluate performance and learning rates in fundamentals of laparoscopic surgery (FLS) and robotic-assisted surgery (RAS). Methods EEG and eye-tracking data were collected from 25 participants performing three FLS and 22 participants performing two RAS tasks. Generalized linear mixed models, using L1-penalized estimation, were developed to objectify performance evaluation using EEG and eye gaze features, and linear models were developed to objectify learning rate evaluation using these features and performance scores at the first attempt. Experience metrics were added to evaluate their role in learning robotic surgery. The differences in performance across experience levels were tested using analysis of variance. Results EEG and eye gaze features and experience-related features were important for evaluating performance in FLS and RAS tasks with reasonable results. Residents outperformed faculty in FLS peg transfer (p value = 0.04), while faculty and residents both excelled over pre-medical students in the FLS pattern cut (p value = 0.01 and p value < 0.001, respectively). Fellows outperformed pre-medical students in FLS suturing (p value = 0.01). In RAS tasks, both faculty and fellows surpassed pre-medical students (p values for the RAS pattern cut were 0.001 for faculty and 0.003 for fellows, while for RAS tissue dissection, the p value was less than 0.001 for both groups), with residents also showing superior skills in tissue dissection (p value = 0.03). Conclusion Findings could be used to develop training interventions for improving surgical skills and have implications for understanding motor learning and designing interventions to enhance learning outcomes. Graphical abstract

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mentioning

confidence: 99%

Performance and learning rate prediction models development in FLS and RAS surgical tasks using electroencephalogram and eye gaze data and machine learning

Shafiei,

Shadpour,

Intes

et al. 2023

Surg Endosc

Self Cite

View full text Add to dashboard Cite

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Surgical skill level classification model development using EEG and eye-gaze data and machine learning algorithms

Shafiei,

Shadpour,

Mohler

et al. 2023

J Robotic Surg

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The aim of this study was to develop machine learning classification models using electroencephalogram (EEG) and eye-gaze features to predict the level of surgical expertise in robot-assisted surgery (RAS). EEG and eye-gaze data were recorded from 11 participants who performed cystectomy, hysterectomy, and nephrectomy using the da Vinci robot. Skill level was evaluated by an expert RAS surgeon using the modified Global Evaluative Assessment of Robotic Skills (GEARS) tool, and data from three subtasks were extracted to classify skill levels using three classification models—multinomial logistic regression (MLR), random forest (RF), and gradient boosting (GB). The GB algorithm was used with a combination of EEG and eye-gaze data to classify skill levels, and differences between the models were tested using two-sample t tests. The GB model using EEG features showed the best performance for blunt dissection (83% accuracy), retraction (85% accuracy), and burn dissection (81% accuracy). The combination of EEG and eye-gaze features using the GB algorithm improved the accuracy of skill level classification to 88% for blunt dissection, 93% for retraction, and 86% for burn dissection. The implementation of objective skill classification models in clinical settings may enhance the RAS surgical training process by providing objective feedback about performance to surgeons and their teachers.

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Mp26-09 eye Movement Behavior Associates With Expertise Level in Robot-Assisted Surgery

Cited by 2 publications

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Performance and learning rate prediction models development in FLS and RAS surgical tasks using electroencephalogram and eye gaze data and machine learning

Performance and learning rate prediction models development in FLS and RAS surgical tasks using electroencephalogram and eye gaze data and machine learning

Surgical skill level classification model development using EEG and eye-gaze data and machine learning algorithms

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