Title: Exploring AI Based Approaches for Post-operative Microvascular Free Flap Monitoring
Speakers: Fiona Smith
Microvascular free flap surgery is a key technique utilised for the reconstruction of complex tissue deficits secondary to trauma and cancer. Whilst overall success rates are very good, this is only achieved because of close postoperative monitoring that allows the prompt identification of complications that require immediate re-operation for salvage. The current gold standard for free flap monitoring is regular clinical assessment of the patient at the beside but this is subjective, very labour intensive and disruptive for patients’ sleep. This PhD project aims to explore AI-based approaches for the postoperative monitoring of microvascular free flaps that are as effective and accurate as clinical assessment at identifying compromised free flaps but which are less subjective. In this talk I will discuss the progress that has been made in the first year towards this aim. Firstly, I will outline the project protocols that have been designed for the acquisition of a free flap database with approvals for AI work. Secondly, I will describe the scoping review of current practices for ethical AI analysis of medical image datasets that was prompted by this. Finally, the results of early experiments to use a convolutional neural network to segment images of free flaps will be discussed and the plans for future work will be given.