Research and Application of Ultrasonic Intelligent Diagnosis System for Ovarian Mass

N/ANot Yet RecruitingNCT06528236

Zhejiang Provincial People's Hospital100,000 enrolled

Overview

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

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

Study Type

OBSERVATIONAL

Enrollment

100,000

Conditions

Ovarian Neoplasms Adnexal Mass

Interventions

Artificial intelligence modelDIAGNOSTIC_TEST

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

Eligibility

Sex: FEMALEHealthy volunteers:

Medical Language ↔ Plain English

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.

Outcomes

Primary Outcomes

Area under the curve

AUC (Area Under the Curve) is a common index used to evaluate the performance of binary classification model.

Time frame: Through study completion, an average of 1 year

Secondary Outcomes

Sensitivity

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

Time frame: Through study completion, an average of 1 year

Central Contacts

Yingnan Wu, Doctor

CONTACT

0086 19883106164 litaosun1971@sina.com

Data from ClinicalTrials.gov

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