Lymph Node Disease Clinical Trial
Official title:
Prediction Model Based on Deep Learning of CP-EBUS Multimodal Image in the Diagnosis of Benign and Malignant Lymph Nodes
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.
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.
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