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J Reconstr Microsurg 2022; 38(05): 420-428
DOI: 10.1055/s-0041-1733995
Original Article

Software-based Detection of Acute Rejection Changes in Face Transplant

Miguel I. Dorante  ‡   *
1   Department of Surgery, Division of Plastic Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
2   Department of Surgery, Division of Plastic and Reconstructive Surgery, Lahey Hospital and Medical Center, Burlington, Massachusetts
,
Branislav Kollar  ‡   *
1   Department of Surgery, Division of Plastic Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
3   Department of Plastic and Hand Surgery, University of Freiburg Medical Center, Medical Faculty of the University of Freiburg, Freiburg, Germany
,
Marian Bittner
4   VicarVision, Amsterdam, The Netherlands
,
Alice Wang 
1   Department of Surgery, Division of Plastic Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
,
Yannick Diehm
1   Department of Surgery, Division of Plastic Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
5   Department of Hand, Plastic and Reconstructive Surgery, Microsurgery, Burn Trauma Center, BG Trauma Center Ludwigshafen, University of Heidelberg, Ludwigshafen, Germany
,
Sina Foroutanjazi
1   Department of Surgery, Division of Plastic Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
,
Neil Parikh 
1   Department of Surgery, Division of Plastic Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
,
Valentin Haug
1   Department of Surgery, Division of Plastic Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
5   Department of Hand, Plastic and Reconstructive Surgery, Microsurgery, Burn Trauma Center, BG Trauma Center Ludwigshafen, University of Heidelberg, Ludwigshafen, Germany
,
Tim M. den Uyl
4   VicarVision, Amsterdam, The Netherlands
,
Bohdan Pomahac
1   Department of Surgery, Division of Plastic Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
6   Department of Surgery, Division of Plastic and Reconstructive Surgery, Yale New Haven Hospital, Yale School of Medicine, New Haven, Connecticut
› Institutsangaben
Funding This study was granted funding to B.P. by the United States Department of Defense under their Reconstructive Transplant Research Program (#W81XWH-18–1-0702). The authors (M.I.D. and B.P.) received partial support from this award. A paid collaboration with VicarVision (Amsterdam, Netherlands) was paid for fully from this award with authors (M.B. and T.M.den U.) receiving support for their work. The Department of Defense had no role in the design and conduct of the study; collection, management, analysis and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. The opinions, interpretations, conclusions, and recommendations in this work are those of the authors and are not necessarily endorsed by the Department of Defense. B.K. was recipient of Plastic Surgery Foundation Research Fellowship Grant outside of submitted work. None of the authors have financial interests in any products or devices mentioned in this manuscript.
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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.

Keywords

vascularized composite allotransplantation - face transplant - rejection - software - facial recognition

* Miguel I. Dorante and Branislav Kollar have contributed equally to this manuscript.




Publikationsverlauf

Eingereicht: 30. April 2021

Angenommen: 19. Juli 2021

Artikel online veröffentlicht:
01. September 2021

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