Harnessing deep learning for real-time prediction of flap viability in microsurgical reconstruction: Current advances and future perspectives

Micro-reconstructive reconstruction is an essential element in reconstructive surgery, often performed using free flap transfer to help restore tissue viability and reduce postoperative morbidity, including conditions such as tissue necrosis and flap necrosis. Clinical observation and Doppler ultrasound are examples of classical monitoring practices, which are, in most cases, not real-time, precise and objective, thereby leading to delayed responses. In this article, the authors discuss the revolutionary impact of deep learning (DL), a subset of Artificial Intelligence, on the real-time prediction of flap viability. DL uses a combination of sophisticated neural networks to work on multimodal information (intraoperative imaging, near-infrared spectroscopy, and physiological signals), providing a reliable and timely evaluation of the tissue perfusion and vascular status. In recent years, convolutional neural networks have been used to analyze flap images and recurrent neural networks have been used to observe perfusion over time, with better sensitivity and specificity than traditional approaches. The technologies also enhance the ability to make decisions during the operation and track results post-operation, which reduces the incidence of flap failure. The future is hoped to see the development of a functional DL architecture, the introduction of more innovative technologies such as augmented reality, the creation of solutions to address matters such as data scarcity and model interpretability, and, finally, the addressing of the legal implications of the developments above. The successful surmounting of these issues makes it possible for DL to receive microsurgery at an individual, information-proximate level, which must enhance clinical outcomes and reduce morbidity among patients. The article also emphasizes the importance of adopting a multidisciplinary approach to address the gap between the emergence of deep learning (DL) and its practical application in the context of future reconstruction surgery.