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Non-Expert Markings of Active Chronic Graft-Versus-Host Disease Photographs: Optimal Metrics of Training Effects.

Kelsey ParksXiaoqi LiuTahsin ReasatZain KheraLaura X BakerHeidi ChenBenoit M DawantInga SakniteEric R Tkaczyk
Published in: Journal of digital imaging (2022)
Lack of reliable measures of cutaneous chronic graft-versus-host disease (cGVHD) remains a significant challenge. Non-expert assistance in marking photographs of active disease could aid the development of automated segmentation algorithms, but validated metrics to evaluate training effects are lacking. We studied absolute and relative error of marked body surface area (BSA), redness, and the Dice index as potential metrics of non-expert improvement. Three non-experts underwent an extensive training program led by a board-certified dermatologist to mark cGVHD in photographs. At the end of the 4-month training, the dermatologist confirmed that each trainee had learned to accurately mark cGVHD. The trainees' inter- and intra-rater intraclass correlation coefficient estimates were "substantial" to "almost perfect" for both BSA and total redness. For fifteen 3D photos of patients with cGVHD, the trainees' median absolute (relative) BSA error compared to expert marking dropped from 20 cm 2 (29%) pre-training to 14 cm 2 (24%) post-training. Total redness error decreased from 122 a*·cm 2 (26%) to 95 a*·cm 2 (21%). By contrast, median Dice index did not reflect improvement (0.76 to 0.75). Both absolute and relative BSA and redness errors similarly and stably reflected improvements from this training program, which the Dice index failed to capture.
Keyphrases
  • virtual reality
  • deep learning
  • clinical practice
  • machine learning
  • magnetic resonance
  • emergency department
  • magnetic resonance imaging
  • primary care
  • general practice