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NCT Number: NCT06528236

Research and Application of Ultrasonic Intelligent Diagnosis System for Ovarian Mass

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

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Key information

Conditions

Ovarian Neoplasms Adnexal Diseases Adnexal Mass Endocrine Gland Neoplasms Endocrine System Diseases Female Urogenital Diseases Female Urogenital Diseases and Pregnancy Complications Genital Diseases Genital Diseases, Female Genital Neoplasms, Female Gonadal Disorders Neoplasms Neoplasms by Site Ovarian Diseases Urogenital Diseases Urogenital Neoplasms

Sex eligibility

Female

Study type

Observational

About this study

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

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

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

Exclusion criteria

  • Histopathological analysis confirms non-ovarian tumor;
  • Histopathological results are inconclusive;
  • 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.

Treatment and study plan

Artificial intelligence model

Diagnostic Test

Using the artificial intelligence model to diagnosis benign, borderline, and malignant ovarian masses.

Primary outcomes

  1. 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.

Secondary outcomes

  1. 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

Other outcomes

  1. 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.

  2. 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

  3. 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

  4. 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

Study contacts

Contact information is provided by the study sponsor or research team.

Yingnan Wu, Doctor

CONTACT

[email protected]

0086 19883106164

Sponsors and collaborators

Lead sponsor

Zhejiang Provincial People's Hospital

Other

Collaborators

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

Registry information

Official study title

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

Important dates

Study start
2024
Primary completion
2029
Study completion
2029
First posted
Jul 30, 2024
Registry last updated
Jul 30, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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