Intelligent Segmentation Algorithm of Ultrasonic Image

November 2, 2024 updated by: Tianzhu Liu, Tongji Hospital
This project aims to enhance the performance of ultrasonic image analysis by optimizing and refining neural network algorithms, while also collecting and constructing extensive datasets relevant to ultrasonic imagery. The algorithm will be trained and evaluated in a data-driven manner, with test results facilitating accurate segmentation of regional block images and identification of characteristic ultrasonic anatomy. This approach will significantly advance the study and development of ultrasonic technology.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Detailed Description

  1. Algorithm development: Develop high-performance intelligent ultrasonic image recognition and segmentation algorithms;
  2. Data set: Establish ultrasound image data set, which is convenient for researchers to develop and verify algorithms;
  3. Apply for patents, publish academic papers, promote and popularize technology.

Study Type

Observational

Enrollment (Estimated)

5000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

  • Name: Liu Tianzhu, M.D.
  • Phone Number: 13098866448
  • Email: liutzh@126.com

Study Locations

  • China
    • Hubei
      • Wuhan, Hubei, China, 430000
        • Recruiting
        • Tianzhu Liu
        • Contact:
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Sampling Method

Non-Probability Sample

Study Population

Any patient undergoing an ultrasound scan, including volunteers.

Description

Inclusion Criteria:

  • None.

Exclusion Criteria:

  • None.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Ultrasonic image
Time Frame: 2 years
Ultrasound images obtained by ultrasound scan
2 years

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Tongji Hospital

Investigators

  • Principal Investigator: Mei Wei, M.D., Tongji Hospital

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

October 14, 2024

Primary Completion (Estimated)

November 2, 2026

Study Completion (Estimated)

November 14, 2026

Study Registration Dates

First Submitted

October 15, 2024

First Submitted That Met QC Criteria

October 15, 2024

First Posted (Actual)

October 17, 2024

Study Record Updates

Last Update Posted (Estimated)

November 5, 2024

Last Update Submitted That Met QC Criteria

November 2, 2024

Last Verified

November 1, 2024

More Information

Terms related to this study

Other Study ID Numbers

  • Tongji Hospital102114-4

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

IPD Plan Description

Contact the researcher if necessary after the study is completed.

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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