A marker-less technique for measuring kinematics in the operating room

Lane L. Frasier; David P. Azari; Yue Ma; Sudha R. Pavuluri Quamme; Robert G. Radwin; Carla M. Pugh; Thomas Y. Yen; Chia Hsiung Chen; Caprice C. Greenberg

doi:10.1016/j.surg.2016.05.004

A marker-less technique for measuring kinematics in the operating room

Lane L. Frasier, David P. Azari, Yue Ma, Sudha R. Pavuluri Quamme, Robert G. Radwin, Carla M. Pugh, Thomas Y. Yen, Chia Hsiung Chen, Caprice C. Greenberg

Research output: Contribution to journal › Article › peer-review

21 Scopus citations

Abstract

Background Often in simulated settings, quantitative analysis of technical skill relies largely on specially tagged instruments or tracers on surgeons’ hands. We investigated a novel, marker-less technique for evaluating technical skill during open operations and for differentiating tasks and surgeon experience level. Methods We recorded the operative field via in-light camera for open operations. Sixteen cases yielded 138 video clips of suturing and tying tasks ≥5 seconds in duration. Video clips were categorized based on surgeon role (attending, resident) and task subtype (suturing tasks: body wall, bowel anastomosis, complex anastomosis; tying tasks: body wall, superficial tying, deep tying). We tracked a region of interest on the hand to generate kinematic data. Nested, multilevel modeling addressed the nonindependence of clips obtained from the same surgeon. Results Interaction effects for suturing tasks were seen between role and task categories for average speed (P = .04), standard deviation of speed (P = .05), and average acceleration (P = .03). There were significant differences across task categories for standard deviation of acceleration (P = .02). Significant differences for tying tasks across task categories were observed for maximum speed (P = .02); standard deviation of speed (P = .04); and average (P = .02), maximum (P < .01), and standard deviation (P = .03) of acceleration. Conclusion We demonstrated the ability to detect kinematic differences in performance using marker-less tracking during open operative cases. Suturing task evaluation was most sensitive to differences in surgeon role and task category and may represent a scalable approach for providing quantitative feedback to surgeons about technical skill.

Original language	English (US)
Pages (from-to)	1400-1413
Number of pages	14
Journal	Surgery (United States)
Volume	160
Issue number	5
DOIs	https://doi.org/10.1016/j.surg.2016.05.004
State	Published - Nov 1 2016
Externally published	Yes

ASJC Scopus subject areas

Surgery

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10.1016/j.surg.2016.05.004

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@article{d02383649e0048609e0639bc05594e0b,

title = "A marker-less technique for measuring kinematics in the operating room",

abstract = "Background Often in simulated settings, quantitative analysis of technical skill relies largely on specially tagged instruments or tracers on surgeons{\textquoteright} hands. We investigated a novel, marker-less technique for evaluating technical skill during open operations and for differentiating tasks and surgeon experience level. Methods We recorded the operative field via in-light camera for open operations. Sixteen cases yielded 138 video clips of suturing and tying tasks ≥5 seconds in duration. Video clips were categorized based on surgeon role (attending, resident) and task subtype (suturing tasks: body wall, bowel anastomosis, complex anastomosis; tying tasks: body wall, superficial tying, deep tying). We tracked a region of interest on the hand to generate kinematic data. Nested, multilevel modeling addressed the nonindependence of clips obtained from the same surgeon. Results Interaction effects for suturing tasks were seen between role and task categories for average speed (P = .04), standard deviation of speed (P = .05), and average acceleration (P = .03). There were significant differences across task categories for standard deviation of acceleration (P = .02). Significant differences for tying tasks across task categories were observed for maximum speed (P = .02); standard deviation of speed (P = .04); and average (P = .02), maximum (P < .01), and standard deviation (P = .03) of acceleration. Conclusion We demonstrated the ability to detect kinematic differences in performance using marker-less tracking during open operative cases. Suturing task evaluation was most sensitive to differences in surgeon role and task category and may represent a scalable approach for providing quantitative feedback to surgeons about technical skill.",

author = "Frasier, {Lane L.} and Azari, {David P.} and Yue Ma and {Pavuluri Quamme}, {Sudha R.} and Radwin, {Robert G.} and Pugh, {Carla M.} and Yen, {Thomas Y.} and Chen, {Chia Hsiung} and Greenberg, {Caprice C.}",

note = "Funding Information: This study has demonstrated the feasibility of obtaining kinematic data for 2 common operative tasks during open operations, without necessitating specialized tracking devices or limiting analysis to laparoscopic cases. Previous work evaluating descriptive differences in dominant versus nondominant hand movements in attending surgeons versus residents 18 demonstrated the feasibility of this approach for open operative cases. Here, we have extended this work and sought to discern differences based on surgeon role and task relationship to the various stages of an operation, with the ultimate aim of identifying high-yield sections of open operations amenable to differentiating skill level and providing feedback metrics to surgeons. Our identification of statistically significant differences across several kinematic measures for 2 tasks encountered in a majority of operative procedures represents a step forward in a scalable methodology to assess operative skill under a variety of actual operative conditions and toward generalization of the ability to measure technical skill. These tasks were evaluated within the context and flow of an entire operative case, rather than evaluating discrete tasks without an operative context, as is seen with simulation benchtop models. Based on the type of task performed, we identified significant differences in speed and acceleration metrics for suturing and tying tasks. Our findings are consistent with prior work in which these tasks consistently differentiated surgeons based on experience level. 11 We also build on recent work that identified differences in idle time, 22 path length, and suture time 23 for suturing tasks performed on models simulating more or less complex tissues. Our approach, however, is novel in that it consistently identified differences between attendings and residents and between task categories, during a wide variety of actual operations, without restricting task parameters commonly used in highly controlled benchtop assessments. Even more exciting was our identification of interaction effects between surgeon role and task category for suturing. In other words, attending surgeons{\textquoteright} suturing kinematics are significantly different when working on different tissue types and when comparing attending and resident surgeons working on the same tissue type. This represents a critical step forward in assessment of operative technical skill, suggests that assessment of suturing technical skill is most sensitive to surgeon experience and complexity of the operation, and represents a promising target for further quantification of technical skill. Interestingly, increasing task complexity did not always correlate with stepwise decreases in speed and acceleration metrics; several S2 (bowel anastomosis) task metrics were lower than S3 (complex anastomosis) measures, indicating that surgeons were slower at bowel anastomosis suturing tasks than they were at sewing more complex anastomoses. This was seen when assessing tasks overall and also when assessing by roles (attending and resident surgeons). Additionally, residents had higher maximum speed, maximum acceleration, standard deviation of speed, and standard deviation of acceleration for S2 tasks compared with attending surgeons. These findings seem to indicate that increasing technical skill does not translate solely to increased speed and acceleration. For example, the decreased acceleration seen in S2 tasks performed by attendings may represent a smoother, steadier pace compared with a less skilled operator who demonstrates pauses and rapid changes in motion, which would lead to increased maximum speed and acceleration. Overall, our findings highlight the unstudied relationships between technical elements (suturing, tying) and the larger context of an operation. Ongoing work is needed to understand the relationships between the kinematic data evaluated here and other assessments of technical skill. It may be that kinematic data assessment is most appropriate for evaluation of certain tasks, such as abdominal fascial closure, while other tasks, such as a complex hepatobiliary anastomosis, are more appropriately evaluated with a global assessment score or a combination of several metrics. Of note, S1 (body wall) suturing tasks were most likely to have interaction or category effects compared with S2 (bowel anastomosis) or S3 (complex anastomosis) tasks. This may be due to the higher number of S1 clips available for analysis, as all operations analyzed required closure of an open abdominal wound, whereas S2 and S3 clips could only be obtained for specific types of operations. There are several limitations to consider. While the in-light camera represents a noninvasive method of video capture, the captured images are dependent on where the operating surgeons focus the boom light, which is not necessarily where they are working. Surgeons' hands moved in and out of the video frame or were obscured by a surgeon's head leaning over the operative field, reducing the data available for technical analysis. These limitations could be resolved with use of a wide-angle lens on a boom separate from the in-light camera, a setup that is becoming increasingly available. The research team could set the angle and location of the camera boom prior to case start and begin recording remotely, minimizing interference with the operative team and ensuring high-quality data capture. Wearable technologies, like GoPro (GoPro, Inc., San Mateo, CA) and mobile video glasses, have also been successfully employed by our group and others. 18,24 Additionally, some components of our current methodology, such as clip identification, were time intensive and not easily scalable. Ongoing evaluation of the kinematic differences between task subcategories (eg, T1 versus T2, S1 versus S3) could lead to the development of software capable of recognizing the kinematic patterns associated with suturing and tying tasks and subtasks, identifying and flagging clips for quick confirmation by the surgeon. Likewise, our distance calibration was time consuming and based on population measures of hand size. Since the in-light camera location and distance from the surgeons{\textquoteright} hands were constantly changing throughout the operation, it was not practical to make precise distance calibrations. Kinematic measures relative to hand dimensions were considered a pragmatic approach to approximating distances, given the relatively low precision of marker-less video tracking of the different hands, and were considered sources of random error. The kinematic measures are therefore approximations and contain some measurement errors, possibly contributing to additional variability and noise in the statistical analysis. Further accuracy could be attained by calibrating based on surgeon glove size; obtaining standardized, one-time measurements of the surgeons{\textquoteright} hands; or including a standardized reference, such as a ruler, in the operative field. Power analysis was not conducted prior to the study, as it was not possible to determine the number of clips that we would have per case. Sample sizes were set by prior experience in this type of analysis. Post hoc power analysis focused on testing the role difference between attending versus resident surgeons in the kinematics for tying tasks, given that no significant role difference was found for any of the kinematic measures. Power analysis was conducted using Optimal Design 3.01 (Optimal Design is open source sponsored by the William T. Grant Foundation, New York, NY), 25 a power analysis software program that takes into account the clustered data structure. Power was influenced by multiple factors, including type I error rate, sample sizes (ie, number of surgeons, number of cases performed per surgeon, and number of video clips included per case), intraclass correlations at surgeon level and at case level, and effect size (ie, the role difference in a kinematic measure in standardized metric). The analysis showed that the standardized role difference observed in the current study (0.11 to 0.47) was smaller than the minimum effect size (0.85 to 1.26) that could be detected with sufficient power (0.80) for all 6 kinematic measures, which could have led to the statistical insignificance. Future studies with increased sample sizes and improved procedures to exclude unusable video clips from analysis would help increase power and allow a greater possibility in detecting small role difference. Video recording of operations is likely to become increasingly commonplace given growing availability and sophistication of video technology and medicine's cultural shift toward increased transparency. 26 This type of marker-less tracking could offer the ability to analyze data and provide feedback to surgeons for a wide variety of open or laparoscopic cases and practice settings, allowing surgeons to obtain information easily on their technical performance as part of ongoing skill development. Specifically, as we further develop our understanding of quantitative skill measurement, kinematic data could provide learners with objective feedback about the kinematics of tasks during new skills and procedures with a measure of their progress toward mastery. This approach combines the use of video, which allows for self-observation and reflection, and the provision of objective, numerical feedback. Ultimately, this methodology could provide a high-throughput, scalable method of providing objective data to surgeons regarding their technical performance in open operations. In time, identifications of kinematic patterns associated with various stages of technical proficiency and kinematics associated with “expert” status could be determined (and, as noted elsewhere, may not correlate solely with high speed and acceleration). These data could provide surgeons with benchmarks with which to compare their data over time. Potential applications include assessment of surgeons during skill acquisition as well as maintenance and could be useful for residency programs and for surgeons re-entering the OR after time away for medical or personal reasons. Prior research indicates that the number of hand movements 9-11 and time taken 9-12,21 decreases for a given task with increased surgical experience. Fewer hand movements, synonymous with increased efficiency, result in smooth, steady hand motion and cycles of motion without hesitation. These concepts are conceptually parallel to higher peak speed and acceleration. Decreased time taken, synonymous with increased speed, is consistent with our findings of increased mean speed and acceleration for suturing tasks. Increased efficiency alone, however, is insufficient to truly evaluate technical skill. A surgeon may move quickly but with poor results—a stitch that pulls through due to poor placement or a dropped knot throw due to moving too quickly. Quality assessment was not performed during this evaluation, as we sought to demonstrate feasibility. Future work must include correlating these kinematic measures with other evaluations, such as global assessment or cosmetic and functional outcomes, to ensure that surgeons are not sacrificing quality for speed. Finally, significant attention in education and human factors has focused on the concept of expertise and the circumstances surrounding experts{\textquoteright} transitions from automated, routine behaviors to more deliberate, effortful evaluation and actions. 27 This transition may occur deliberately, at preidentified stages in the operation related to patient- or procedure-specific characteristics, or with intraoperative identification of an unexpected difficulty or roadblock to proceeding further. This change in cognitive processes has been labeled “slowing down,” but there are no data exploring how this transition affects kinematic movements during an operation. We feel that this represents a critical avenue of future investigation. Such transitions are included in hidden Markov models. The work we are doing now can inform development of such multivariable and multidimensions models. Further work is needed to determine whether the kinematics of highly demanding portions of the case can vary from more routine, automated portions and how they might change with expected versus unexpected task complexity. Description of markers identifying these high-intensity periods could provide surgeons with another tool for thoughtful reflection and self-assessment and allow surgeons to anticipate when a transition to deliberate, effortful activity may be needed in future cases. It is very possible that the transitions in speed or acceleration will be more predictive of performance than absolute measures. Furthermore, this methodology could identify changes in motion kinematics as a marker of potentially difficult or high-risk periods of an operation. This could eventually allow for rapid processing of large volumes of operative video and selective manual review of points at highest risk for safety compromise. The next steps for this work include comparison of technical kinematic data with global assessment, such as the Objective Structured Assessment of Technical Skill, and identification of kinematic patterns associated with varying degrees of proficiency, development of a scalable measurement of surgeons{\textquoteright} hands, and further assessment of the relationships between skill, speed and acceleration, and case complexity. We would like to acknowledge Linda Yang, BS, for her work on this project. Publisher Copyright: {\textcopyright} 2016 Elsevier Inc.",

year = "2016",

month = nov,

day = "1",

doi = "10.1016/j.surg.2016.05.004",

language = "English (US)",

volume = "160",

pages = "1400--1413",

journal = "Surgery (United States)",

issn = "0039-6060",

publisher = "Mosby Inc.",

number = "5",

}

TY - JOUR

T1 - A marker-less technique for measuring kinematics in the operating room

AU - Frasier, Lane L.

AU - Azari, David P.

AU - Ma, Yue

AU - Pavuluri Quamme, Sudha R.

AU - Radwin, Robert G.

AU - Pugh, Carla M.

AU - Yen, Thomas Y.

AU - Chen, Chia Hsiung

AU - Greenberg, Caprice C.

N1 - Funding Information: This study has demonstrated the feasibility of obtaining kinematic data for 2 common operative tasks during open operations, without necessitating specialized tracking devices or limiting analysis to laparoscopic cases. Previous work evaluating descriptive differences in dominant versus nondominant hand movements in attending surgeons versus residents 18 demonstrated the feasibility of this approach for open operative cases. Here, we have extended this work and sought to discern differences based on surgeon role and task relationship to the various stages of an operation, with the ultimate aim of identifying high-yield sections of open operations amenable to differentiating skill level and providing feedback metrics to surgeons. Our identification of statistically significant differences across several kinematic measures for 2 tasks encountered in a majority of operative procedures represents a step forward in a scalable methodology to assess operative skill under a variety of actual operative conditions and toward generalization of the ability to measure technical skill. These tasks were evaluated within the context and flow of an entire operative case, rather than evaluating discrete tasks without an operative context, as is seen with simulation benchtop models. Based on the type of task performed, we identified significant differences in speed and acceleration metrics for suturing and tying tasks. Our findings are consistent with prior work in which these tasks consistently differentiated surgeons based on experience level. 11 We also build on recent work that identified differences in idle time, 22 path length, and suture time 23 for suturing tasks performed on models simulating more or less complex tissues. Our approach, however, is novel in that it consistently identified differences between attendings and residents and between task categories, during a wide variety of actual operations, without restricting task parameters commonly used in highly controlled benchtop assessments. Even more exciting was our identification of interaction effects between surgeon role and task category for suturing. In other words, attending surgeons’ suturing kinematics are significantly different when working on different tissue types and when comparing attending and resident surgeons working on the same tissue type. This represents a critical step forward in assessment of operative technical skill, suggests that assessment of suturing technical skill is most sensitive to surgeon experience and complexity of the operation, and represents a promising target for further quantification of technical skill. Interestingly, increasing task complexity did not always correlate with stepwise decreases in speed and acceleration metrics; several S2 (bowel anastomosis) task metrics were lower than S3 (complex anastomosis) measures, indicating that surgeons were slower at bowel anastomosis suturing tasks than they were at sewing more complex anastomoses. This was seen when assessing tasks overall and also when assessing by roles (attending and resident surgeons). Additionally, residents had higher maximum speed, maximum acceleration, standard deviation of speed, and standard deviation of acceleration for S2 tasks compared with attending surgeons. These findings seem to indicate that increasing technical skill does not translate solely to increased speed and acceleration. For example, the decreased acceleration seen in S2 tasks performed by attendings may represent a smoother, steadier pace compared with a less skilled operator who demonstrates pauses and rapid changes in motion, which would lead to increased maximum speed and acceleration. Overall, our findings highlight the unstudied relationships between technical elements (suturing, tying) and the larger context of an operation. Ongoing work is needed to understand the relationships between the kinematic data evaluated here and other assessments of technical skill. It may be that kinematic data assessment is most appropriate for evaluation of certain tasks, such as abdominal fascial closure, while other tasks, such as a complex hepatobiliary anastomosis, are more appropriately evaluated with a global assessment score or a combination of several metrics. Of note, S1 (body wall) suturing tasks were most likely to have interaction or category effects compared with S2 (bowel anastomosis) or S3 (complex anastomosis) tasks. This may be due to the higher number of S1 clips available for analysis, as all operations analyzed required closure of an open abdominal wound, whereas S2 and S3 clips could only be obtained for specific types of operations. There are several limitations to consider. While the in-light camera represents a noninvasive method of video capture, the captured images are dependent on where the operating surgeons focus the boom light, which is not necessarily where they are working. Surgeons' hands moved in and out of the video frame or were obscured by a surgeon's head leaning over the operative field, reducing the data available for technical analysis. These limitations could be resolved with use of a wide-angle lens on a boom separate from the in-light camera, a setup that is becoming increasingly available. The research team could set the angle and location of the camera boom prior to case start and begin recording remotely, minimizing interference with the operative team and ensuring high-quality data capture. Wearable technologies, like GoPro (GoPro, Inc., San Mateo, CA) and mobile video glasses, have also been successfully employed by our group and others. 18,24 Additionally, some components of our current methodology, such as clip identification, were time intensive and not easily scalable. Ongoing evaluation of the kinematic differences between task subcategories (eg, T1 versus T2, S1 versus S3) could lead to the development of software capable of recognizing the kinematic patterns associated with suturing and tying tasks and subtasks, identifying and flagging clips for quick confirmation by the surgeon. Likewise, our distance calibration was time consuming and based on population measures of hand size. Since the in-light camera location and distance from the surgeons’ hands were constantly changing throughout the operation, it was not practical to make precise distance calibrations. Kinematic measures relative to hand dimensions were considered a pragmatic approach to approximating distances, given the relatively low precision of marker-less video tracking of the different hands, and were considered sources of random error. The kinematic measures are therefore approximations and contain some measurement errors, possibly contributing to additional variability and noise in the statistical analysis. Further accuracy could be attained by calibrating based on surgeon glove size; obtaining standardized, one-time measurements of the surgeons’ hands; or including a standardized reference, such as a ruler, in the operative field. Power analysis was not conducted prior to the study, as it was not possible to determine the number of clips that we would have per case. Sample sizes were set by prior experience in this type of analysis. Post hoc power analysis focused on testing the role difference between attending versus resident surgeons in the kinematics for tying tasks, given that no significant role difference was found for any of the kinematic measures. Power analysis was conducted using Optimal Design 3.01 (Optimal Design is open source sponsored by the William T. Grant Foundation, New York, NY), 25 a power analysis software program that takes into account the clustered data structure. Power was influenced by multiple factors, including type I error rate, sample sizes (ie, number of surgeons, number of cases performed per surgeon, and number of video clips included per case), intraclass correlations at surgeon level and at case level, and effect size (ie, the role difference in a kinematic measure in standardized metric). The analysis showed that the standardized role difference observed in the current study (0.11 to 0.47) was smaller than the minimum effect size (0.85 to 1.26) that could be detected with sufficient power (0.80) for all 6 kinematic measures, which could have led to the statistical insignificance. Future studies with increased sample sizes and improved procedures to exclude unusable video clips from analysis would help increase power and allow a greater possibility in detecting small role difference. Video recording of operations is likely to become increasingly commonplace given growing availability and sophistication of video technology and medicine's cultural shift toward increased transparency. 26 This type of marker-less tracking could offer the ability to analyze data and provide feedback to surgeons for a wide variety of open or laparoscopic cases and practice settings, allowing surgeons to obtain information easily on their technical performance as part of ongoing skill development. Specifically, as we further develop our understanding of quantitative skill measurement, kinematic data could provide learners with objective feedback about the kinematics of tasks during new skills and procedures with a measure of their progress toward mastery. This approach combines the use of video, which allows for self-observation and reflection, and the provision of objective, numerical feedback. Ultimately, this methodology could provide a high-throughput, scalable method of providing objective data to surgeons regarding their technical performance in open operations. In time, identifications of kinematic patterns associated with various stages of technical proficiency and kinematics associated with “expert” status could be determined (and, as noted elsewhere, may not correlate solely with high speed and acceleration). These data could provide surgeons with benchmarks with which to compare their data over time. Potential applications include assessment of surgeons during skill acquisition as well as maintenance and could be useful for residency programs and for surgeons re-entering the OR after time away for medical or personal reasons. Prior research indicates that the number of hand movements 9-11 and time taken 9-12,21 decreases for a given task with increased surgical experience. Fewer hand movements, synonymous with increased efficiency, result in smooth, steady hand motion and cycles of motion without hesitation. These concepts are conceptually parallel to higher peak speed and acceleration. Decreased time taken, synonymous with increased speed, is consistent with our findings of increased mean speed and acceleration for suturing tasks. Increased efficiency alone, however, is insufficient to truly evaluate technical skill. A surgeon may move quickly but with poor results—a stitch that pulls through due to poor placement or a dropped knot throw due to moving too quickly. Quality assessment was not performed during this evaluation, as we sought to demonstrate feasibility. Future work must include correlating these kinematic measures with other evaluations, such as global assessment or cosmetic and functional outcomes, to ensure that surgeons are not sacrificing quality for speed. Finally, significant attention in education and human factors has focused on the concept of expertise and the circumstances surrounding experts’ transitions from automated, routine behaviors to more deliberate, effortful evaluation and actions. 27 This transition may occur deliberately, at preidentified stages in the operation related to patient- or procedure-specific characteristics, or with intraoperative identification of an unexpected difficulty or roadblock to proceeding further. This change in cognitive processes has been labeled “slowing down,” but there are no data exploring how this transition affects kinematic movements during an operation. We feel that this represents a critical avenue of future investigation. Such transitions are included in hidden Markov models. The work we are doing now can inform development of such multivariable and multidimensions models. Further work is needed to determine whether the kinematics of highly demanding portions of the case can vary from more routine, automated portions and how they might change with expected versus unexpected task complexity. Description of markers identifying these high-intensity periods could provide surgeons with another tool for thoughtful reflection and self-assessment and allow surgeons to anticipate when a transition to deliberate, effortful activity may be needed in future cases. It is very possible that the transitions in speed or acceleration will be more predictive of performance than absolute measures. Furthermore, this methodology could identify changes in motion kinematics as a marker of potentially difficult or high-risk periods of an operation. This could eventually allow for rapid processing of large volumes of operative video and selective manual review of points at highest risk for safety compromise. The next steps for this work include comparison of technical kinematic data with global assessment, such as the Objective Structured Assessment of Technical Skill, and identification of kinematic patterns associated with varying degrees of proficiency, development of a scalable measurement of surgeons’ hands, and further assessment of the relationships between skill, speed and acceleration, and case complexity. We would like to acknowledge Linda Yang, BS, for her work on this project. Publisher Copyright: © 2016 Elsevier Inc.

PY - 2016/11/1

Y1 - 2016/11/1

N2 - Background Often in simulated settings, quantitative analysis of technical skill relies largely on specially tagged instruments or tracers on surgeons’ hands. We investigated a novel, marker-less technique for evaluating technical skill during open operations and for differentiating tasks and surgeon experience level. Methods We recorded the operative field via in-light camera for open operations. Sixteen cases yielded 138 video clips of suturing and tying tasks ≥5 seconds in duration. Video clips were categorized based on surgeon role (attending, resident) and task subtype (suturing tasks: body wall, bowel anastomosis, complex anastomosis; tying tasks: body wall, superficial tying, deep tying). We tracked a region of interest on the hand to generate kinematic data. Nested, multilevel modeling addressed the nonindependence of clips obtained from the same surgeon. Results Interaction effects for suturing tasks were seen between role and task categories for average speed (P = .04), standard deviation of speed (P = .05), and average acceleration (P = .03). There were significant differences across task categories for standard deviation of acceleration (P = .02). Significant differences for tying tasks across task categories were observed for maximum speed (P = .02); standard deviation of speed (P = .04); and average (P = .02), maximum (P < .01), and standard deviation (P = .03) of acceleration. Conclusion We demonstrated the ability to detect kinematic differences in performance using marker-less tracking during open operative cases. Suturing task evaluation was most sensitive to differences in surgeon role and task category and may represent a scalable approach for providing quantitative feedback to surgeons about technical skill.

AB - Background Often in simulated settings, quantitative analysis of technical skill relies largely on specially tagged instruments or tracers on surgeons’ hands. We investigated a novel, marker-less technique for evaluating technical skill during open operations and for differentiating tasks and surgeon experience level. Methods We recorded the operative field via in-light camera for open operations. Sixteen cases yielded 138 video clips of suturing and tying tasks ≥5 seconds in duration. Video clips were categorized based on surgeon role (attending, resident) and task subtype (suturing tasks: body wall, bowel anastomosis, complex anastomosis; tying tasks: body wall, superficial tying, deep tying). We tracked a region of interest on the hand to generate kinematic data. Nested, multilevel modeling addressed the nonindependence of clips obtained from the same surgeon. Results Interaction effects for suturing tasks were seen between role and task categories for average speed (P = .04), standard deviation of speed (P = .05), and average acceleration (P = .03). There were significant differences across task categories for standard deviation of acceleration (P = .02). Significant differences for tying tasks across task categories were observed for maximum speed (P = .02); standard deviation of speed (P = .04); and average (P = .02), maximum (P < .01), and standard deviation (P = .03) of acceleration. Conclusion We demonstrated the ability to detect kinematic differences in performance using marker-less tracking during open operative cases. Suturing task evaluation was most sensitive to differences in surgeon role and task category and may represent a scalable approach for providing quantitative feedback to surgeons about technical skill.

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A marker-less technique for measuring kinematics in the operating room

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