Research and Application of Ultrasonic Intelligent Diagnosis System for Ovarian Mass

July 25, 2024 updated by: Zhejiang Provincial People's Hospital

Research on Automatic Detection of Ovarian Mass and Intelligent Auxiliary Diagnosis System Based on Multimodal Ultrasound Images

Research on automatic detection of ovarian mass and intelligent auxiliary diagnosis system based on multimodal ultrasound images.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Detailed Description

Investigators aimed to develop an ultrasonic intelligent diagnosis system for ovarian mass based on multimodal ultrasound images.

Study Type

Observational

Enrollment (Estimated)

100000

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

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

During gynecological ultrasound examination, at least one patient with persistent ovarian tumor was found. The patient underwent surgical treatment and the histopathological results.

Description

Inclusion Criteria:

  1. During gynecological ultrasound examination, at least one patient with persistent ovarian tumor was found.
  2. The patient underwent surgical treatment and the histopathological results.

Exclusion Criteria:

  1. Histopathological analysis confirms non-ovarian tumor;
  2. Histopathological results are inconclusive;
  3. Issues with image quality: the ovarian mass is incomplete and does not show some surrounding tissues (but the mass is too large to exclude completely); the images are overly blurry, making it difficult to determine the characteristics of the ovarian mass (possible reasons include hardware quality issues with the ultrasound machine, motion blur, focusing problems, presence of intestinal gas in the patient); gain settings make it difficult to judge the characteristics of the ovarian mass (such as low contrast, excessively dark images, or saturation); the presence of artifacts affects the assessment of ultrasound characteristics of the ovarian mass and should be excluded.

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

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Training cohort
Training cohort is used to training artificial model based on multimodel ultrasound images or videos.
Validation cohort
Validation cohort is used to validate artificial model.
Diagnostic test: Artificial intelligence model
Using the artificial intelligence model to diagnosis benign, borderline, and malignant ovarian masses.
Internal test cohort
Internal test cohort is used to internally test artificial model.
Diagnostic test: Artificial intelligence model
Using the artificial intelligence model to diagnosis benign, borderline, and malignant ovarian masses.
External test cohort
External test cohort is used to internally test artificial model.
Diagnostic test: Artificial intelligence model
Using the artificial intelligence model to diagnosis benign, borderline, and malignant ovarian masses.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Area under the curve
Time Frame: Through study completion, an average of 1 year
AUC (Area Under the Curve) is a common index used to evaluate the performance of binary classification model.
Through study completion, an average of 1 year

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Sensitivity
Time Frame: Through study completion, an average of 1 year
Sensitivity refers to the ability of the test to correctly identify a positive result in an individual who actually has the disease. It represents the proportion of cases in which the test is able to detect a positive for the disease
Through study completion, an average of 1 year

Other Outcome Measures

Outcome Measure
Measure Description
Time Frame
Specificity
Time Frame: Through study completion, an average of 1 year
Specificity refers to the ability of the test to correctly identify a negative result in an individual who does not actually have the disease. It represents the proportion of cases where the disease is negative that the test is able to detect.
Through study completion, an average of 1 year
Accuracy
Time Frame: Through study completion, an average of 1 year
Accuracy refers to the degree to which the results of the diagnostic test are consistent with the actual situation
Through study completion, an average of 1 year
Positive predicative value
Time Frame: Through study completion, an average of 1 year
Positive Predictive Value indicates the probability that a test result will be true if it is positive. In other words, it represents the proportion of individuals who are diagnosed as positive when the test result is positive who actually have the disease
Through study completion, an average of 1 year
Negative predictive value
Time Frame: Through study completion, an average of 1 year
Negative Predictive Value refers to the probability that if a test result is negative, the result will be true negative. It represents the proportion of individuals who are diagnosed as negative when the test results are negative that are truly free of the disease
Through study completion, an average of 1 year

Collaborators and Investigators

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

Sponsor

Zhejiang Provincial People's Hospital

Collaborators

The Affiliated Hospital of Qingdao University
Women's Hospital School Of Medicine Zhejiang University
The Second Affiliated Hospital of Harbin Medical University
Sichuan provincial maternity and child health care hospital
Aksu First People's Hospital

Investigators

  • Study Chair: Litao Sun, Professor, Zhejiang provincial people's 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 (Estimated)

July 30, 2024

Primary Completion (Estimated)

July 30, 2029

Study Completion (Estimated)

July 30, 2029

Study Registration Dates

First Submitted

July 18, 2024

First Submitted That Met QC Criteria

July 25, 2024

First Posted (Actual)

July 30, 2024

Study Record Updates

Last Update Posted (Actual)

July 30, 2024

Last Update Submitted That Met QC Criteria

July 25, 2024

Last Verified

July 1, 2024

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • KY2024053

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

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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