PURPOSE: To objectively assess suturing performance using an image analysis program and to provide validity evidence for this assessment method by comparing experts' and novices' performance. METHOD: In 2009, the authors used an image analysis program to extract objective variables from digital images of suturing end products obtained during a previous study involving third-year medical students (novices) and surgical faculty and residents (experts). Variables included number of stitches, stitch length, total bite size, travel, stitch orientation, total bite-size-to-travel ratio, and symmetry across the incision ratio. The authors compared all variables between groups to detect significant differences and two variables (total bite-size-to-travel ratio and symmetry across the incision ratio) to ideal values. RESULTS: Five experts and 15 novices participated. Experts' and novices' performances differed significantly (P < .05) with large effect sizes attributable to experience (Cohen d > 0.8) for total bite size (P = .009, d = 1.5), travel (P = .045, d = 1.1), total bite-size-to-travel ratio (P < .0001, d = 2.6), stitch orientation (P = .014,d = 1.4), and symmetry across the incision ratio (P = .022, d = 1.3). CONCLUSIONS: The authors found that a simple computer algorithm can extract variables from digital images of a running suture and rapidly provide quantitative summative assessment feedback. The significant differences found between groups confirm that this system can discriminate between skill levels. This image analysis program represents a viable training tool for objectively assessing trainees' suturing, a foundational skill for many medical specialties.
PURPOSE: To objectively assess suturing performance using an image analysis program and to provide validity evidence for this assessment method by comparing experts' and novices' performance. METHOD: In 2009, the authors used an image analysis program to extract objective variables from digital images of suturing end products obtained during a previous study involving third-year medical students (novices) and surgical faculty and residents (experts). Variables included number of stitches, stitch length, total bite size, travel, stitch orientation, total bite-size-to-travel ratio, and symmetry across the incision ratio. The authors compared all variables between groups to detect significant differences and two variables (total bite-size-to-travel ratio and symmetry across the incision ratio) to ideal values. RESULTS: Five experts and 15 novices participated. Experts' and novices' performances differed significantly (P < .05) with large effect sizes attributable to experience (Cohen d > 0.8) for total bite size (P = .009, d = 1.5), travel (P = .045, d = 1.1), total bite-size-to-travel ratio (P < .0001, d = 2.6), stitch orientation (P = .014,d = 1.4), and symmetry across the incision ratio (P = .022, d = 1.3). CONCLUSIONS: The authors found that a simple computer algorithm can extract variables from digital images of a running suture and rapidly provide quantitative summative assessment feedback. The significant differences found between groups confirm that this system can discriminate between skill levels. This image analysis program represents a viable training tool for objectively assessing trainees' suturing, a foundational skill for many medical specialties.
Authors: David P Azari; Lane L Frasier; Sudha R Pavuluri Quamme; Caprice C Greenberg; Carla M Pugh; Jacob A Greenberg; Robert G Radwin Journal: Ann Surg Date: 2019-03 Impact factor: 12.969