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Task-Level vs. Segment-Level Quantitative Metrics for Surgical Skill Assessment.
Vedula, S Swaroop; Malpani, Anand; Ahmidi, Narges; Khudanpur, Sanjeev; Hager, Gregory; Chen, Chi Chiung Grace.
Afiliação
  • Vedula SS; Department of Computer Science, Johns Hopkins University, Baltimore, Maryland. Electronic address: vedula@jhu.edu.
  • Malpani A; Department of Computer Science, Johns Hopkins University, Baltimore, Maryland.
  • Ahmidi N; Department of Computer Science, Johns Hopkins University, Baltimore, Maryland.
  • Khudanpur S; Department of Electrical & Computer Engineering, Johns Hopkins University, Baltimore, Maryland.
  • Hager G; Department of Computer Science, Johns Hopkins University, Baltimore, Maryland.
  • Chen CC; Department of Gynecology & Obstetrics, Johns Hopkins University School of Medicine, Baltimore, Maryland.
J Surg Educ ; 73(3): 482-9, 2016.
Article em En | MEDLINE | ID: mdl-26896147
OBJECTIVE: Task-level metrics of time and motion efficiency are valid measures of surgical technical skill. Metrics may be computed for segments (maneuvers and gestures) within a task after hierarchical task decomposition. Our objective was to compare task-level and segment (maneuver and gesture)-level metrics for surgical technical skill assessment. DESIGN: Our analyses include predictive modeling using data from a prospective cohort study. We used a hierarchical semantic vocabulary to segment a simple surgical task of passing a needle across an incision and tying a surgeon's knot into maneuvers and gestures. We computed time, path length, and movements for the task, maneuvers, and gestures using tool motion data. We fit logistic regression models to predict experience-based skill using the quantitative metrics. We compared the area under a receiver operating characteristic curve (AUC) for task-level, maneuver-level, and gesture-level models. SETTING: Robotic surgical skills training laboratory. PARTICIPANTS: In total, 4 faculty surgeons with experience in robotic surgery and 14 trainee surgeons with no or minimal experience in robotic surgery. RESULTS: Experts performed the task in shorter time (49.74s; 95% CI = 43.27-56.21 vs. 81.97; 95% CI = 69.71-94.22), with shorter path length (1.63m; 95% CI = 1.49-1.76 vs. 2.23; 95% CI = 1.91-2.56), and with fewer movements (429.25; 95% CI = 383.80-474.70 vs. 728.69; 95% CI = 631.84-825.54) than novices. Experts differed from novices on metrics for individual maneuvers and gestures. The AUCs were 0.79; 95% CI = 0.62-0.97 for task-level models, 0.78; 95% CI = 0.6-0.96 for maneuver-level models, and 0.7; 95% CI = 0.44-0.97 for gesture-level models. There was no statistically significant difference in AUC between task-level and maneuver-level (p = 0.7) or gesture-level models (p = 0.17). CONCLUSIONS: Maneuver-level and gesture-level metrics are discriminative of surgical skill and can be used to provide targeted feedback to surgical trainees.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Estudos de Tempo e Movimento / Técnicas de Sutura / Competência Clínica / Procedimentos Cirúrgicos Robóticos Idioma: En Ano de publicação: 2016 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Estudos de Tempo e Movimento / Técnicas de Sutura / Competência Clínica / Procedimentos Cirúrgicos Robóticos Idioma: En Ano de publicação: 2016 Tipo de documento: Article