Proficiency Assessment of Gesture Analysis in Laparoscopy by Means of the Surgeon's Musculo-Skeleton Model

Cavallo, Filippo; Pietrabissa, Andrea; Megali, Giuseppe; Troia, Elena; Sinigaglia, S.; Dario, P.; Mosca, Franco; Cuschieri, Alfred

doi:10.1097/sla.0b013e318238350e

Cited by 6 publications

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“…Again low loads on muscles were observed among expert surgeons (see Figure 4). More details about this approach can be found in Cavallo et al 13 The evaluation metric S m is defined as the value of the log-likelihood of a generic performance compared to the expert model. The log-likelihood value gives a measure of the probability that a generic performance is generated by the expert model.…”

Section: Resultsmentioning

confidence: 99%

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Biomechanics–Machine Learning System for Surgical Gesture Analysis and Development of Technologies for Minimal Access Surgery

et al. 2013

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Section: Resultsmentioning

confidence: 99%

“…For the virtual reality setting, we used a well-validated commercial laparoscopic simulator, LapSim Basic Skills 3.0 (Surgical Science AB, Goteborg, Sweden), as previously reported. 13 The set of 5 exercises, described in Table 2, was chosen.…”

Section: Experimental Protocolsmentioning

confidence: 99%

Biomechanics–Machine Learning System for Surgical Gesture Analysis and Development of Technologies for Minimal Access Surgery

et al. 2013

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“…Therefore, automating the process of surgical skill evaluation may be necessary, and thus far, automation approaches have shown promise. However, although numerous efforts have been made to develop automatic surgical skill assessments (ASSAs), most of these methods require specific types of kinetics data, such as tool motion tracking, [5][6][7] hand motion tracking, 8,9 eye motion tracking, 10,11 and muscle contraction analysis, 12 and sensors or markers are required for data extraction.…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of a 3-Dimensional Convolutional Neural Network for Automatic Surgical Skill Assessment Based on Spatiotemporal Video Analysis

et al. 2021

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IMPORTANCE A high level of surgical skill is essential to prevent intraoperative problems. One important aspect of surgical education is surgical skill assessment, with pertinent feedback facilitating efficient skill acquisition by novices.OBJECTIVES To develop a 3-dimensional (3-D) convolutional neural network (CNN) model for automatic surgical skill assessment and to evaluate the performance of the model in classification tasks by using laparoscopic colorectal surgical videos. DESIGN, SETTING, AND PARTICIPANTSThis prognostic study used surgical videos acquired prior to 2017. In total, 650 laparoscopic colorectal surgical videos were provided for study purposes by the Japan Society for Endoscopic Surgery, and 74 were randomly extracted. Every video had highly reliable scores based on the Endoscopic Surgical Skill Qualification System (ESSQS, range 1-100, with higher scores indicating greater surgical skill) established by the society. Data were analyzed June to December 2020. MAIN OUTCOMES AND MEASURESFrom the groups with scores less than the difference between the mean and 2 SDs, within the range spanning the mean and 1 SD, and greater than the sum of the mean and 2 SDs, 17, 26, and 31 videos, respectively, were randomly extracted. In total, 1480 video clips with a length of 40 seconds each were extracted for each surgical step (medial mobilization, lateral mobilization, inferior mesenteric artery transection, and mesorectal transection) and separated into 1184 training sets and 296 test sets. Automatic surgical skill classification was performed based on spatiotemporal video analysis using the fully automated 3-D CNN model, and classification accuracies and screening accuracies for the groups with scores less than the mean minus 2 SDs and greater than the mean plus 2 SDs were calculated. RESULTSThe mean (SD) ESSQS score of all 650 intraoperative videos was 66.2 (8.6) points and for the 74 videos used in the study, 67.6 (16.1) points. The proposed 3-D CNN model automatically classified video clips into groups with scores less than the mean minus 2 SDs, within 1 SD of the mean, and greater than the mean plus 2 SDs with a mean (SD) accuracy of 75.0% (6.3%). The highest accuracy was 83.8% for the inferior mesenteric artery transection. The model also screened for the group with scores less than the mean minus 2 SDs with 94.1% sensitivity and 96.5% specificity and for group with greater than the mean plus 2 SDs with 87.1% sensitivity and 86.0% specificity. CONCLUSIONS AND RELEVANCEThe results of this prognostic study showed that the proposed 3-D CNN model classified laparoscopic colorectal surgical videos with sufficient accuracy to be used for screening groups with scores greater than the mean plus 2 SDs and less than the mean minus 2 SDs. The proposed approach was fully automatic and easy to use for various types of surgery, and no (continued) Key Points Question Is it possible to apply deep learning-based spatiotemporal video analysis using a 3-dimensional convolutional neural network to automate surgical skill ...

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“…Standard endoscopic surgery is associated with major musculoskeletal discomfort [4], [5], [6], [7], [8]. Park et al showed that 87% of 317 laparoscopic surgeons reported physical strain or symptoms of discomfort [8].…”

Section: Introductionmentioning

confidence: 99%

Ergonomic assessment of the da Vinci console in robot-assisted surgery

Hullenaar

Hermans

Broeders

2017

Innovative Surgical Sciences

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Background: Robot-assisted surgery is considered to improve ergonomics over standard endoscopic surgery. Nevertheless, previous research demonstrated ergonomic deficits in the current console set-up. Aim: This study was designed to objectively assess body posture in the da Vinci console during robot-assisted endoscopic surgery. Methods: Multiple sagittal photographs from six physicians were taken during robot-assisted procedures. Trunk, neck, shoulder, elbow, hip, and knee angles were calculated and compared to ergonomic preferable joint angles. A 2D geometric model was developed using individual anthropometrics. Optimal seat height, armrest height, and viewer height were calculated. These results were compared to the findings of the sagittal photographs. Results: Mean joint angles show potentially harmful neck angles for all participants. Trunk angles vary between surgeons, from inadequate to correct. In short and very tall individuals, optimal armrest height is outside the adjustment range of the console. Conclusion: The da Vinci Surgical System console seating position results in a nonergonomic neck and trunk angle. The developed geometric model revealed that armrest height has a limited adjustment range. Adjustments to the console and optimization of preoperative settings are goals to further improve ergonomics in robot-assisted surgery.

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Proficiency Assessment of Gesture Analysis in Laparoscopy by Means of the Surgeon's Musculo-Skeleton Model

Abstract: The model provides data on the evaluation of biomechanical parameters of surgical gesture not only in kinematic terms but also includes analysis of the dynamics of muscle contraction analysis during surgical manipulations.

Cited by 6 publications

References 4 publications

Biomechanics–Machine Learning System for Surgical Gesture Analysis and Development of Technologies for Minimal Access Surgery

Biomechanics–Machine Learning System for Surgical Gesture Analysis and Development of Technologies for Minimal Access Surgery

Development and Validation of a 3-Dimensional Convolutional Neural Network for Automatic Surgical Skill Assessment Based on Spatiotemporal Video Analysis

Ergonomic assessment of the da Vinci console in robot-assisted surgery

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