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:
2025-12-24 @ 3:33 PM
Ignite Modification Date:
2025-12-24 @ 3:33 PM
NCT ID:
NCT04328792
Brief Summary:
Endobronchial ultrasound (EBUS) multimodal image including grey scale, blood flow doppler and elastography, can be used as non-invasive diagnosis and supplement the pathological result, which has important clinical application value. In this study, EBUS multimodal image database of 1000 inthoracic benign and malignant lymph nodes (LNs) will be constructed to train deep learning neural networks, which can automatically select representative images and diagnose LNs. Investigators will establish an artificial intelligence prediction model based on deep learning of intrathoracic LNs, and verify the model in other 300 LNs.
Detailed Description:
Intrathoracic LNs enlargement has a wide range of diseases, among which intrathoracic LNs metastasis of lung cancer is the most common malignant disease. Benign lesions, including inflammation, tuberculosis and sarcoidosis, also need to be differentiated for targeted treatment.
EBUS multimodal image including grey scale, blood flow doppler and elastography, can be used as non-invasive diagnosis and supplement the pathological result, which has important clinical application value. This study includes two parts: retrospectively construction of EBUS artificial intelligence prediction model and multi-center prospectively validation of the prediction model. A total of 1300 LNs will be enrolled in the study.
During the retention of videos, target LNs and peripheral vessels are examined using ultrasound hosts (EU-ME2, Olympus or Hi-vision Avius, Hitachi) equipped with elastography and doppler functions and ultrasound bronchoscopy (BF-UC260FW, Olympus or EB1970UK, Pentax). Multimodal image data of target LNs are collected.
Investigators will construct artificial intelligence prediction model based on deep learning using images from 1000 LNs firstly, and verify the model in other 300 LNs. This model will be compared with traditional qualitative and quantitative evaluation methods to verify the diagnostic efficacy.
Study:
NCT04328792
Study Brief:
Protocol Section:
NCT04328792