Background: An objective, non-invasive method for redness detection during acute allograft rejection in face transplantation (FT) is lacking.
Methods: A retrospective cohort study was performed with 688 images of 7 patients with face transplant (range, 1 to 108 months post-transplant). Healthy controls were matched to donor age, sex, and had no prior facial procedures. Rejection state was confirmed via tissue biopsy. An image-analysis software developed alongside VicarVision (Amsterdam, Netherlands) was used to produce R, a measure of differences between detectable color and absolute red. R is inversely proportional to redness, where lower R values correspond to increased redness. Linear mixed models were used to study fixed effect of rejection state on R values. Estimated marginal means of fitted models were calculated for pairwise comparisons.
Results: Of 688 images, 175, 170, 202, and 141 images were attributable to Banff Grade 0,1,2, and 3, respectively. Estimated change in R value of facial allografts decreased with increasing Banff Grade (p = 0.0001). The mean R value of clinical rejection (Banff Grade ⅔) (16.67, 95% Confidence Interval [CI] 14.79-18.58) was lower (p = 0.005) than non-rejection (Banff Grade 0/1) (19.38, 95%CI 17.43-21.33). Both clinical and non-rejection mean R values were lower (p = 0.0001) than healthy controls (24.12, 95%CI 20.96-27.28).
Conclusion: This proof-of-concept study demonstrates that software-based analysis can detect and monitor acute rejection changes in FT. Future studies should expand on this tool's potential application in telehealth and as a screening tool for allograft rejection.
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