Background: We tested the workflow and comparability of compression garments (CG) automatically knitted from 3D-body-scan data (3DBSD) versus manually measured data for scar treatment. Industry 4.0 has found its way into surgery, enhancing the trend toward personalized medicine, which plays an increasingly important role in CG scar therapy. Therefore, we conducted a study to evaluate the workflow from 3DBSD to fast and precisely knitted CG and compared it with standard of care.
Methods: A randomized controlled crossover feasibility study was conducted as part of the individual medical technology research project "Smart Scar Care." Objective and patient-reported outcome measures were documented for 10 patients with hypertrophic burn scars at baseline and after wearing CG automatically knitted from 3DBSD versus CG from manually measured data for one month.
Results: The "scan-to-knit" workflow and the study design were feasible in 10 of 10 patients. No adverse effects were found. 3DBSD showed a bias of half a centimeter compared with manually measured data and wider limits of agreement. With respect to fit, comfort, suitability, Vancouver Scar Scale, Patient and Observer Scar Assessment Scale, stiffness and microcirculation, this was a promising pilot study. Stiffness and blood flow were increased in scars compared with normal skin. The highest rank correlations were found between pain and itch, stiffness and Patient and Observer Scar Assessment Scale, Vancouver Scar Scale, and pain.
Conclusions: These results indicate that automatically knitted CG using 3DBSD could become an alternative to the standard of care, especially with regard to economical and faster patient care. The produced scan data opens the door for objective scar science.
Copyright © 2021 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of The American Society of Plastic Surgeons.