Description Module
The Description Module contains narrative descriptions of the clinical trial, including a brief summary and detailed description. These descriptions provide important information about the study's purpose, methodology, and key details in language accessible to both researchers and the general public.
Description Module path is as follows:
Study -> Protocol Section -> Description Module
Description Module
Ignite Creation Date:
2025-12-25 @ 2:29 AM
Ignite Modification Date:
2025-12-25 @ 2:29 AM
NCT ID:
NCT06504134
Brief Summary:
Gastroesophageal reflux disease (GERD) is a very common condition in clinical practice. In China, GERD affects nearly 150 million patients, whose quality of life are seriously impacted. Currently, the diagnosis of GERD primarily depends on the results of 24h reflux monitoring. However, such examination is under a quite low acceptability. As a result, a large number of patients were not diagnosed timely and accurately, and serious social problems are induced, such as drug abuse of proton pump inhibitor. Our team has previously developed a novel device for esophageal cell enrichment and established an internationally pioneering method of cytological screening for esophageal cancer based on cutting-edge deep learning technology. This project aims to develop multiple deep learning algorithms and establish an innovative method for diagnosis of GRED, using the novel esophageal cell enrichment technology. The research includes: 1) constructing deep learning algorithms for automatic esophageal inflammatory cells recognition and classification; 2) mining and extracting the key features of esophageal squamous cells and inflammatory cells under physician-AI interaction; 3) establishing a prediction model for GERD by integrating digital features of squamous cells and inflammatory cells and building a cloud-based automatic diagnosis system; 4) investigating the immuno-infiltration atlas of GERD and its diagnostic value based on the enriched inflammatory cells. The ultimate goal is to solve current clinical problems and realize rapid, convenient, and accurate diagnosis of GERD.
Study:
NCT06504134
Study Brief:
Protocol Section:
NCT06504134