Software-based Detection of Acute Rejection Changes in Face Transplant

Miguel I. Dorante; Branislav Kollar; Marian Bittner; Alice Wang; Yannick Diehm; Sina Foroutanjazi; Neil Parikh; Valentin Haug; Tim M. den Uyl; Bohdan Pomahac

doi:10.1055/s-0041-1733995

Journal of Reconstructive Microsurgery, Inhaltsverzeichnis

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 ‡^*

¹Department of Surgery, Division of Plastic Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts

²Department of Surgery, Division of Plastic and Reconstructive Surgery, Lahey Hospital and Medical Center, Burlington, Massachusetts

,

Branislav Kollar ‡^*

¹Department of Surgery, Division of Plastic Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts

³Department of Plastic and Hand Surgery, University of Freiburg Medical Center, Medical Faculty of the University of Freiburg, Freiburg, Germany

,

Marian Bittner

⁴VicarVision, Amsterdam, The Netherlands

,

Alice Wang

¹Department of Surgery, Division of Plastic Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts

,

Yannick Diehm

¹Department of Surgery, Division of Plastic Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts

⁵Department of Hand, Plastic and Reconstructive Surgery, Microsurgery, Burn Trauma Center, BG Trauma Center Ludwigshafen, University of Heidelberg, Ludwigshafen, Germany

,

Sina Foroutanjazi

¹Department of Surgery, Division of Plastic Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts

,

Neil Parikh

¹Department of Surgery, Division of Plastic Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts

,

Valentin Haug

¹Department of Surgery, Division of Plastic Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts

⁵Department of Hand, Plastic and Reconstructive Surgery, Microsurgery, Burn Trauma Center, BG Trauma Center Ludwigshafen, University of Heidelberg, Ludwigshafen, Germany

,

Tim M. den Uyl

⁴VicarVision, Amsterdam, The Netherlands

,

Bohdan Pomahac

¹Department of Surgery, Division of Plastic Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts

⁶Department of Surgery, Division of Plastic and Reconstructive Surgery, Yale New Haven Hospital, Yale School of Medicine, New Haven, Connecticut

› Institutsangaben

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

Volltext

Referenzen

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