Support vector machines improve the accuracy of evaluation for the performance of laparoscopic training tasks

Despite technological advances in the tracking of surgical motions, automatic evaluation of laparoscopic skills remains remote. A new method is proposed that combines multiple discrete motion analysis metrics. This new method is compared with previously proposed metric combination methods and shown to provide greater ability for classifying novice and expert surgeons. For this study, 30 participants (four experts and 26 novices) performed 696 trials of three training tasks: peg transfer, pass rope, and cap needle. Instrument motions were recorded and reduced to four metrics. Three methods of combining metrics into a prediction of surgical competency (summed-ratios, z-score normalization, and support vector machine [SVM]) were compared. The comparison was based on the area under the receiver operating characteristic curve (AUC) and the predictive accuracy with a previously unseen validation data set. For all three tasks, the SVM method was superior in terms of both AUC and predictive accuracy with the validation set. The SVM method resulted in AUCs of 0.968, 0.952, and 0.970 for the three tasks compared respectively with 0.958, 0.899, and 0.884 for the next best method (weighted z-normalization). The SVM method correctly predicted 93.7, 91.3, and 90.0% of the subjects’ competencies, whereas the weighted z-normalization respectively predicted 86.6, 79.3, and 75.7% accurately (p < 0.002). The findings show that an SVM-based analysis provides more accurate predictions of competency at laparoscopic training tasks than previous analysis techniques. An SVM approach to competency evaluation should be considered for computerized laparoscopic performance evaluation systems

Definitions for loss of domain: an international Delphi consensus of expert surgeons

Background No standardized written or volumetric definition exists for ‘loss of domain’ (LOD). This limits the utility of LOD as a morphological descriptor and as a predictor of peri- and postoperative outcomes. Consequently, our aim was to establish definitions for LOD via consensus of expert abdominal wall surgeons. Methods A Delphi study involving 20 internationally recognized abdominal wall reconstruction (AWR) surgeons was performed. Four written and two volumetric definitions of LOD were identified via systematic review. Panelists completed a questionnaire that suggested these definitions as standardized definitions of LOD. Consensus on a preferred term was pre-defined as achieved when selected by ≥80% of panelists. Terms scoring <20% were removed. Results Voting commenced August 2018 and was completed in January 2019. Written definition: During Round 1, two definitions were removed and seven new definitions were suggested, leaving nine definitions for consideration. For Round 2, panelists were asked to select all appealing definitions. Thereafter, common concepts were identified during analysis, from which the facilitators advanced a new written definition. This received 100% agreement in Round 3. Volumetric definition: Initially, panelists were evenly split, but consensus for the Sabbagh method was achieved. Panelists could not reach consensus regarding a threshold LOD value that would preclude surgery. Conclusions Consensus for written and volumetric definitions of LOD was achieved from 20 internationally recognized AWR surgeons. Adoption of these definitions will help standardize the use of LOD for both clinical and academic activities

Definitions for Loss of Domain: An International Delphi Consensus of Expert Surgeons.

No standardized written or volumetric definition exists for 'loss of domain' (LOD). This limits the utility of LOD as a morphological descriptor and as a predictor of peri- and postoperative outcomes. Consequently, our aim was to establish definitions for LOD via consensus of expert abdominal wall surgeons. A Delphi study involving 20 internationally recognized abdominal wall reconstruction (AWR) surgeons was performed. Four written and two volumetric definitions of LOD were identified via systematic review. Panelists completed a questionnaire that suggested these definitions as standardized definitions of LOD. Consensus on a preferred term was pre-defined as achieved when selected by ≥80% of panelists. Terms scoring  Voting commenced August 2018 and was completed in January 2019. Written definition: During Round 1, two definitions were removed and seven new definitions were suggested, leaving nine definitions for consideration. For Round 2, panelists were asked to select all appealing definitions. Thereafter, common concepts were identified during analysis, from which the facilitators advanced a new written definition. This received 100% agreement in Round 3. Volumetric definition: Initially, panelists were evenly split, but consensus for the Sabbagh method was achieved. Panelists could not reach consensus regarding a threshold LOD value that would preclude surgery. Consensus for written and volumetric definitions of LOD was achieved from 20 internationally recognized AWR surgeons. Adoption of these definitions will help standardize the use of LOD for both clinical and academic activities