Deep Learning for Flap Perfusion Detection (DL-FPA)

September 23, 2026 updated by: Jing Xu

Application of a Deep Learning-Based Model for Detecting Flap Perfusion Abnormalities After First-Stage Auricular Reconstruction in Congenital Microtia Patients

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.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Study Type

Interventional

Enrollment (Estimated)

232

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • China
      • Shanghai, China, 201112
        • EYE & ENT Hospital, Fudan University
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

6 years to 40 years (Child, Adult)

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Description

Inclusion Criteria:

  • Age 6-40 years;
  • Typical microtia morphology consistent with the diagnostic criteria in Chinese Plastic Surgery;
  • Patients after first-stage total auricular reconstruction who have not yet undergone second-stage surgery;
  • The participant or family member is able to use a smartphone.

Exclusion Criteria:

  • Abnormal coagulation or other relevant laboratory findings;
  • Absence of normal fascial coverage on the affected ear, or presence of eczema, infection, or other local abnormalities on the affected side;
  • Severe cognitive impairment or psychiatric disorders;
  • Significant dysfunction of major organs;
  • The patient or family member does not consent to participation in the study.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Diagnostic
  • Allocation: Non-Randomized
  • Interventional Model: Sequential Assignment
  • Masking: Single

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
No Intervention: Control group

Patients with congenital microtia will undergo first-stage auricular reconstruction with standard flap assessment and routine follow-up, without AI-based remote flap image monitoring.
Experimental: Intervention group

Participants will undergo first-stage auricular reconstruction and receive AI-assisted remote flap monitoring postoperatively, with scheduled image acquisition and flap assessment conducted according to the study protocol.
Behavioral: AI-assisted remote monitoring of flap perfusion abnormalities after first-stage auricular reconstruction

In addition to routine outpatient follow-up, an AI image recognition-based remote intelligent monitoring system led by specialist nurses will be implemented. After discharge, patients will receive daily prompts to upload photographs of the reconstructed ear. The AI system will analyse images in real time: if classified as normal, it will provide standard care recommendations; if classified as abnormal, it will automatically alert the clinical team. Specialist nurses will manually review abnormal images and, as appropriate, request repeat imaging, intensify home care and increase monitoring frequency, or report to the supervising physician and instruct the patient to return to the hospital for further management.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Complication rate
Time Frame: Assessed 3 months postoperatively

Complications will be identified and recorded based on outpatient review notes and follow-up records. The complication rate will be calculated as:

Complication rate = (number of patients with complications after first-stage auricular reconstruction / total number of patients) × 100%.

The following common postoperative complications after first-stage auricular reconstruction will be included: hematoma requiring re-hemostasis, infection, flap necrosis, cartilage exposure, and deformation of the auricular framework.
Assessed 3 months postoperatively

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Patient satisfaction
Time Frame: Assessed 3 months postoperatively

Patient satisfaction will be assessed using a self-designed satisfaction questionnaire comprising 15 items divided into five domains: consultation process and environment (3 items), quality of medical and nursing services (3 items), attitude of medical and nursing staff (3 items), health education (3 items), and use of the case management platform (3 items). Each item will be rated on a 3-point Likert scale from "dissatisfied" to "satisfied," scored 1-3, yielding a total score range of 15-45. The satisfaction score will be calculated as the sum of all item scores, with higher scores indicating greater patient satisfaction.
Assessed 3 months postoperatively
System complication detection rate
Time Frame: Assessed at 3 months postoperatively

The system complication detection rate will be calculated as:

System complication detection rate = (number of cases classified as abnormal by the system and clinically confirmed to have auricular reconstruction complications / total number of cases classified as abnormal by the system) × 100%.
Assessed at 3 months postoperatively

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Jing Xu

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

September 22, 2026

Primary Completion (Estimated)

December 31, 2027

Study Completion (Estimated)

December 31, 2028

Study Registration Dates

First Submitted

September 23, 2026

First Submitted That Met QC Criteria

September 23, 2026

First Posted (Actual)

September 29, 2026

Study Record Updates

Last Update Posted (Actual)

September 29, 2026

Last Update Submitted That Met QC Criteria

September 23, 2026

Last Verified

September 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • Flap Recognition Model

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

IPD Plan Description

Individual participant data will not be shared with external researchers due to patient privacy protection and institutional data policy. Aggregated study results will be reported in publications.

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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