跳至主要内容
临床试验/NCT05819099
NCT05819099尚未招募不适用

The Role of Artificial Intelligence in Endoscopic Diagnosis of Esophagogastric Junctional Adenocarcinoma:A Single Center, Case-control, Diagnostic Study

Qilu Hospital of Shandong University0 个研究点目标入组 200 人开始时间: 2023年12月最近更新:
适应症

试验速览

阶段
不适用
状态
尚未招募
入组人数
200
主要终点
Specificity

研究概览

简要总结

This is a single center, case-control, diagnostic study.The aim of this study is to use deep learning methods to retrospectively analyze the imaging data of gastrointestinal endoscopy in Qilu Hospital, and construct an artificial intelligence model based on endoscopic images for detecting and determining the depth of invasion of esophagogastric junctional adenocarcinoma.This study will also compare the established AI model with the diagnostic results of endoscopists to evaluate the clinical auxiliary value of the model for endoscopists.The research includes stages such as data collection and preprocessing, artificial intelligence model development, model testing and evaluation. The gastroscopy image dataset constructed by this research institute mainly includes three modes of endoscopic imaging: white light endoscopy, optical enhancement endoscopy (OE), and narrowband imaging endoscopy (NBI).

研究设计

研究类型
Observational
观察模型
Case Control
时间视角
Retrospective

入排标准

年龄范围
18 Years 至 75 Years(Adult, Older Adult)
性别
All
接受健康志愿者

入选标准

  • This study included endoscopic images of patients aged 18 and above who underwent endoscopic examination or treatment
  • All patients in the case group need to be pathologically confirmed as esophageal gastric junction adenocarcinoma, and a pathologist has conducted a standardized pathological evaluation of the tumor classification of the lesion, including the overall appearance, size, differentiation type, depth of infiltration, presence or absence of lymphatic/vascular invasion, surgical margin status, etc.
  • The endoscopic images of the control group patients need to be confirmed by biopsy pathology or at least two experienced endoscopists (with operating experience>5000 cases) to jointly confirm that they have clear benign manifestations

排除标准

  • The patient has a previous history of endoscopic treatment or surgery for the esophageal gastric junction.
  • Necessary clinical information cannot be provided during the research process (patient age, gender, lesion characteristics, endoscopic manifestations, endoscopic images, etc.)
  • Low quality endoscopic images, such as those severely affected by bleeding, aperture, blurring, defocusing, artifacts, or excessive mucus after biopsy.

结局指标

主要结局

Specificity

时间窗: 36 months

The researchers calculated the specificity of the established AI model and compared it with endoscopists of different levels.

Positive predictive value

时间窗: 36 months

The researchers calculated the positive predictive value of the established AI model and compared it with endoscopists of different levels.

Sensitivity

时间窗: 36 months

The researchers calculated the sensitivity of the established AI model and compared it with endoscopists of different levels.

Accuracy

时间窗: 36 months

The researchers calculated accuracy positive predictive value of the established AI model and compared it with endoscopists of different levels.

Negative predictive value

时间窗: 36 months

The researchers calculated the negative predictive value of the established AI model and compared it with endoscopists of different levels.

次要结局

未报告次要终点

研究者

申办方类型
Other
责任方
Sponsor

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