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 @ 7:10 PM
Ignite Modification Date:
2025-12-24 @ 7:10 PM
NCT ID:
NCT04957303
Brief Summary:
Machine learning has been widely used in clinical medicine in recent years. It can be used for disease classification, disease severity grading, genetic testing, image analysis, adjuvant treatment recommendations, and predicting patient prognosis. Because sublingual microcirculation can be used for guiding shock resuscitation, a real time automated analysis is required for rapid changes of clinical condition. This study aims to use machine learning to analyze the parameters and patterns of sublingual microcirculation.
Detailed Description:
The sublingual microcirculation videos are extracted from the 11 clinical trials conducting in the National Taiwan University Hospital.
In the first stage, the microcirculation videos and the related information are included in a de-identified manner. Each microcirculation video in the database will have a unique code. The video-related data will include the patient's height, weight, blood pressure, heartbeats, health status, major diseases, laboratory examination values, video quality description, automated vascular analysis (AVA) 3 software analysis results including total vessel density (TVD), perfused vessel density (PVD), proportion of perfused vessels (PPV), microvascular flow index (MFI), and heterogeneity index (HI). The length of each micro-cycle video is 4-6 seconds, and there are 25 frames per second. Take a picture as a representative image, each video can correspond to 4 images, and each micro-circulation image will also be marked with its image quality. Machine learning model will be trained for distinguishing the quality of videos and images. Only good-quality videos and images will be used for further analysis.
In the second stage, 80% of the microcirculation videos and images will be used for training and validation to find the best model, and then the remaining 20% of microcirculation videos and images will be used to test the model performance. The first training purpose is to automatically distinguish the size of blood vessels, calculate TVD, and draw a histogram of the number of microvessels of different diameters. The second training purpose is to measure the blood flow velocity in each small vessel and calculate PVD, MFI, and HI values.
Study:
NCT04957303
Study Brief:
Protocol Section:
NCT04957303