Software-based Detection of Acute Rejection Changes in Face Transplant

Abstract

Abstract 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.