此页面是自动翻译的，不保证翻译的准确性。请参阅 英文版 对于源文本。

Deep Learning for Flap Perfusion Detection (DL-FPA)

2026年9月23日 更新者：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.

研究概览

地位

尚未招聘

条件

干预/治疗

研究类型

介入性

注册 (估计的)

232

阶段

  • 不适用

联系人和位置

本节提供了进行研究的人员的详细联系信息，以及有关进行该研究的地点的信息。

学习联系方式

学习地点

  • 中国
      • Shanghai、中国、201112
        • EYE & ENT Hospital, Fudan University
        • 接触：

参与标准

研究人员寻找符合特定描述的人，称为资格标准。这些标准的一些例子是一个人的一般健康状况或先前的治疗。

资格标准

适合学习的年龄

6年 至 40年 (孩子、成人)

接受健康志愿者

不

有资格学习的性别

全部

描述

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.

学习计划

本节提供研究计划的详细信息，包括研究的设计方式和研究的衡量标准。

研究是如何设计的？

设计细节

  • 主要用途：诊断
  • 分配：非随机化
  • 介入模型：顺序分配
  • 屏蔽：单身的

武器和干预

参与者组/臂
干预/治疗
无干预：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.
实验性的：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.
行为的：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.

研究衡量的是什么？

主要结果指标

结果测量
措施说明
大体时间
Complication rate
大体时间：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

次要结果测量

结果测量
措施说明
大体时间
Patient satisfaction
大体时间：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
大体时间：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

合作者和调查者

在这里您可以找到参与这项研究的人员和组织。

赞助

Jing Xu

出版物和有用的链接

负责输入研究信息的人员自愿提供这些出版物。这些可能与研究有关。

一般刊物

研究记录日期

这些日期跟踪向 ClinicalTrials.gov 提交研究记录和摘要结果的进度。研究记录和报告的结果由国家医学图书馆 (NLM) 审查，以确保它们在发布到公共网站之前符合特定的质量控制标准。

研究主要日期

学习开始 (估计的)

2026年9月22日

初级完成 (估计的)

2027年12月31日

研究完成 (估计的)

2028年12月31日

研究注册日期

首次提交

2026年9月23日

首先提交符合 QC 标准的

2026年9月23日

首次发布 (实际的)

2026年9月29日

研究记录更新

最后更新发布 (实际的)

2026年9月29日

上次提交的符合 QC 标准的更新

2026年9月23日

最后验证

2026年9月1日

更多信息

与本研究相关的术语

关键字

其他相关的 MeSH 术语

其他研究编号

  • Flap Recognition Model

计划个人参与者数据 (IPD)

计划共享个人参与者数据 (IPD)？

不

IPD 计划说明

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.

药物和器械信息、研究文件

研究美国 FDA 监管的药品

不

研究美国 FDA 监管的设备产品

不

此信息直接从 clinicaltrials.gov 网站检索，没有任何更改。如果您有任何更改、删除或更新研究详细信息的请求，请联系 register@clinicaltrials.gov. clinicaltrials.gov 上实施更改，我们的网站上也会自动更新.

订阅