Deep Learning for Flap Perfusion Detection

The aim of this study is to develop a convolutional neural network-based image recognition model to identify flap perfusion abnormalities after first-stage auricular reconstruction in patients with congenital microtia. In addition, the study seeks to evaluate the real-world performance of this model when applied to clinical flap monitoring. The main questions addressed are: * Can an artificial intelligence (AI) image recognition model accurately detect flap perfusion abnormalities from photographs of the reconstructed ear? * Compared with conventional clinical monitoring, does AI-based image recognition alter the incidence of flap-related complications? * How accurate is AI-based detection of flap abnormalities, and how satisfied are patients and healthcare providers with this monitoring approach? The study is divided into two phases. * In phase I, investigators will collect postoperative photographs of reconstructed ears and relevant clinical data from patients with congenital microtia undergoing first-stage auricular reconstruction. Images will be annotated based on the clinical judgement of experienced healthcare professionals, and these data will be used to train a convolutional neural network image recognition model. The model's ability to identify flap perfusion abnormalities will then be assessed through internal validation. * In phase II, the clinical performance of traditional monitoring methods will be compared with AI-based image recognition monitoring. Patients admitted between July 2027 and December 2027 who meet the inclusion criteria will receive conventional clinical flap monitoring and routine follow-up and will comprise the control group. Patients admitted between January 2028 and June 2028 who meet the inclusion criteria will receive flap monitoring using the convolutional neural network-based image recognition module in addition to standard clinical management and follow-up and will comprise the study group. Investigators will compare postoperative complication rates, the accuracy of detecting flap perfusion abnormalities, and satisfaction among patients and healthcare providers between the two groups. Participants will: * Undergo flap monitoring after first-stage auricular reconstruction according to the clinical follow-up schedule; * Provide, or cooperate in the acquisition of, reconstructed ear photographs and relevant clinical data as required by the study; * Receive routine follow-up and health education; * In the study group, undergo flap monitoring assisted by the image recognition module in addition to conventional monitoring.