Application possibilities of artificial intelligence in facial vascularized composite allotransplantation-a narrative review

Abstract

Facial vascularized composite allotransplantation (FVCA) is an emerging field of reconstructive surgery that represents a dogmatic shift in the surgical treatment of patients with severe facial disfigurements. While conventional reconstructive strategies were previously considered the goldstandard for patients with devastating facial trauma, FVCA has demonstrated promising short- and long-term outcomes. Yet, there remain several obstacles that complicate the integration of FVCA procedures into the standard workflow for facial trauma patients. Artificial intelligence (AI) has been shown to provide targeted and resource-effective solutions for persisting clinical challenges in various specialties. However, there is a paucity of studies elucidating the combination of FVCA and AI to overcome such hurdles. Here, we delineate the application possibilities of AI in the field of FVCA and discuss the use of AI technology for FVCA outcome simulation, diagnosis and prediction of rejection episodes, and malignancy screening. This line of research may serve as a fundament for future studies linking these two revolutionary biotechnologies.

Keywords: AI; VCA; artificial intelligence; deep learning; face transplant; facial VCA; machine learning; vascularized composite allotransplantation.

Figure 1

Artificial intelligence (AI) is an umbrella term for various computational strategies including machine learning, robotics, or cognitive computing. Note that, there is no scientific consensus on how to subcategorize AI and classifications may vary between different authors.

Figure 2

The combination of artificial intelligence and facial vascularized composite allotransplantation carries promising potential in advancing preoperative outcome simulation, prediction and diagnosis of rejection episodes, and cancer screening.