Validity and reliability evidence support task-specific metrics for laparoscopic fundoplication.

BACKGROUND: Laparoscopic hiatal hernia repair (LHHR) is a complex operation requiring advanced surgical training. Surgical simulation offers a potential solution for learning complex operations without the need for high surgical volume. Our goal is to develop a virtual reality (VR) simulator for LHHR; however, data supporting task-specific metrics for this procedure are lacking. The purpose of this study was to develop and assess validity and reliability evidence of task-specific metrics for the fundoplication phase of LHHR. METHODS: In phase I, structured interviews with expert foregut surgeons were conducted to develop task-specific metrics (TSM). In phase II, participants with varying levels of surgical expertise performed a laparoscopic Nissen fundoplication procedure on a porcine stomach explant. Video recordings were independently assessed by two blinded graders using global and TSM. An intraclass correlation coefficient (ICC) was used to assess interrater reliability (IRR). Performance scores were compared using a Kruskal-Wallis test. Spearman's rank correlation was used to evaluate the association between global and TSM. RESULTS: Phase I of the study consisted of 12 interviews with expert foregut surgeons. Phase II engaged 31 surgery residents, a fellow, and 6 attendings in the simulation. Phase II results showed high IRR for both global (ICC = 0.84, p < 0.001) and TSM (ICC = 0.75, p < 0.001). Significant between-group differences were detected for both global (χ2 = 24.01, p < 0.001) and TSM (χ2 = 18.4, p < 0.001). Post hoc analysis showed significant differences in performance between the three groups for both metrics (p < 0.05). There was a strong positive correlation between the global and TSM (rs = 0.86, p < 0.001). CONCLUSION: We developed task-specific metrics for LHHR and using a fundoplication model, we documented significant reliability and validity evidence. We anticipate that these LHHR task-specific metrics will be useful in our planned VR simulator.

Automated segmentation of phases, steps, and tasks in laparoscopic cholecystectomy using deep learning.

BACKGROUND: Video-based review is paramount for operative performance assessment but can be laborious when performed manually. Hierarchical Task Analysis (HTA) is a well-known method that divides any procedure into phases, steps, and tasks. HTA requires large datasets of videos with consistent definitions at each level. Our aim was to develop an AI model for automated segmentation of phases, steps, and tasks for laparoscopic cholecystectomy videos using a standardized HTA. METHODS: A total of 160 laparoscopic cholecystectomy videos were collected from a publicly available dataset known as cholec80 and from our own institution. All videos were annotated for the beginning and ending of a predefined set of phases, steps, and tasks. Deep learning models were then separately developed and trained for the three levels using a 3D Convolutional Neural Network architecture. RESULTS: Four phases, eight steps, and nineteen tasks were defined through expert consensus. The training set for our deep learning models contained 100 videos with an additional 20 videos for hyperparameter optimization and tuning. The remaining 40 videos were used for testing the performance. The overall accuracy for phases, steps, and tasks were 0.90, 0.81, and 0.65 with the average F1 score of 0.86, 0.76 and 0.48 respectively. Control of bleeding and bile spillage tasks were most variable in definition, operative management, and clinical relevance. CONCLUSION: The use of hierarchical task analysis for surgical video analysis has numerous applications in AI-based automated systems. Our results show that our tiered method of task analysis can successfully be used to train a DL model.