Machine Learning-assisted Analysis of Microcirculation Pa... | NCT04957303

Studies
Machine Learning-assisted Analysis of Microcirculation Patterns and Parameters

NCT04957303

Sponsor

National Taiwan University Hospital

Status

Unknown status

Last Update Posted

Jul 13, 2022Actual

Enrollment

800Estimated

Phase

Not provided

Conditions

Sublingual Microcirculation Pattern and Parameter Analysis

Interventions

sublingual microcirculation video recording

Countries

Taiwan

Protocol Section

Identification Module Status Module Sponsor/Collaborators Module Oversight Module Description Module Conditions Module Design Module Arms/Interventions Module Outcomes Module Eligibility Module Contacts/Locations Module References Module IPD Sharing Statement Module

Results Section

Participant Flow Module Baseline Characteristics Module Outcome Measures Module Adverse Events Module More Info Module

Annotation Section

Annotation Module

Document Section

Large Document Module

Derived Section

Miscellaneous Info Module Condition Browse Module Intervention Browse Module

Protocol Section

Identification Module

NCT ID

Results Section

No data available

No data is available for this block.

Annotation Section

No data available

No data is available for this block.

Document Section

No data available

No data is available for this block.

Derived Section

Miscellaneous Info Module

Version Holder

Status Module

Record Verification Date

Jul 2022

Overall Recruitment Status or Expanded Access Status

Unknown status

Last Known Status

Recruiting

Delayed Posting

Not provided

Why Stopped

Not provided

Expanded Access Info

Start Date

Aug 3, 2020Actual

Primary Completion Date

Dec 2023Estimated

Completion Date

Apr 2024Estimated

First Submitted Date

Jun 23, 2021

First Submission Date that Met QC Criteria

Jun 30, 2021

First Posted Date

Jul 12, 2021Actual

Results Waived

Not provided

Results First Submitted Date

Not provided

Results First Submitted that Met QC Criteria

Not provided

Results First Posted Date

Not provided

Certification/Extension (aka Delayed Results) First Submitted Date

Not provided

Certification/Extension First Submitted that Passed QC Review

Not provided

Certification/Extension First Posted Date

Not provided

Last Update Submitted Date

Jul 11, 2022

Last Update Posted Date

Jul 13, 2022Actual

Oversight Module

Has Data Monitoring Committee (DMC)

Is FDA Regulated Drug

Is FDA Regulated Device

Is Unapproved Device

Not provided

Pediatric Postmarket Surveillance of a Device Product

Not provided

Product Exported from US

Not provided

FDAAA801 Violation

Not provided

Description Module

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.

Design Module

Study Type

Observational

Number of References to an Expanded Access Study

Not provided

Expanded Access Types

Not provided

Patient Registry

Target Follow-Up Duration

Not provided

Phases

Not provided

Interventional Study Design

Allocation

Not provided

Intervention Model

Biospecimen

No data available

No data is available for this block.

Enrollment

800Estimated

Arms/Interventions Module

Arm Groups

Not provided

Interventions

Name	Type	Description	Arm Group Labels	Other Names
sublingual microcirculation video recording	Device	The sublingual microcirculation videos were recorded by video microscope

Outcomes Module

Primary Outcomes

Measure	Description	Time Frame
Perfused vessel density	Training machine learning models to view the videos of patients' sublingual microcirculation images and calculate the perfused vessel density. The videos of patients' sublingual microcirculation images are obtained and recorded by the video microscopes.	6 seconds

Secondary Outcomes

Measure	Description	Time Frame
Patterns of microcirculation	Training machine learning models to view the videos of patients' microcirculation images and distinguish the patterns of microcirculation images and videos among healthy volunteers and patients with specific diseases or clinical conditions (eg. dialysis, postoperative, or septic shock.) The videos of patients' sublingual microcirculation images are obtained and recorded by the video microscopes.

Other Outcomes

Not provided

Eligibility Module

Eligibility Criteria

Inclusion Criteria:

Microcirculation videos and images from previous clinical trials in the National Taiwan University Hospital with signed informed consent and agreement of further analysis

Exclusion Criteria:

Microcirculation videos and images from previous clinical trials in the National Taiwan University Hospital with signed informed consent but disagreement of further analysis.

Accepts Healthy Volunteers

Yes

Sex

All

Sex/Gender Based

Not provided

Sex/Gender Description

Not provided

Minimum Age

20 Years

Maximum Age

Not provided

Standard Ages

AdultOlder Adult

Study Population

Healthy volunteers Critically ill patients Surgical patients Patients with dialysis

Sampling Method

Non-Probability Sample

Contacts/Locations Module

Central Contacts

Name	Role	Phone	Extension	Email
Yu-Chang Yeh, MD, PhD	Contact	+886-9-68661829		tonyyeh@ntuh.gov.tw

Overall Officials

Not provided

Locations

Facility	Status	City	State	ZIP	Country	Contacts
National Taiwan University Hospital	Recruiting	Taipei		10002	Taiwan

IPD Sharing Statement Module

Plan to Share IPD

Description

Not provided

Types

Not provided

Time Frame

Not provided

Access Criteria

Not provided

URL

Not provided