Clinical Research Directory

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.

Case Management for Microtia

The aim of this study is to develop an intelligent, full-course case management model for patients with microtia, enabling them to receive continuous, individualized nursing and health management services across the stages from diagnosis and treatment to rehabilitation. The study seeks to address the following questions: What practical needs do patients with microtia and their families have throughout the treatment process? How can case management and an information-based platform be used to provide continuous, personalized health management for these patients? Does implementation of an intelligent case management model improve patient health education, follow-up management and care experience? What will participants be asked to do? During microtia-related diagnosis and treatment, Participants will: Establish a personal health record documenting disease- and treatment-related information; Receive stage-specific, individualized health education and nursing guidance; Obtain treatment reminders, follow-up notifications and rehabilitation tasks via an information platform; Complete corresponding health management tasks under the guidance of healthcare professionals; Communicate online with healthcare providers through the platform and provide timely feedback on treatment and rehabilitation; Undergo scheduled follow-up and outcome assessments. Through these procedures, the research team will integrate patients' fragmented needs for diagnosis, treatment, nursing and rehabilitation at different stages, forming a continuous management process that spans the entire course of care.