Research and Application of Ultrasonic Intelligent Diagnosis System for Ovarian Mass

Not yet recruiting

• Conditions: Ovarian Neoplasms; Adnexal Mass
• Interventions: Diagnostic Test: Artificial intelligence model

• Registration Number: NCT06528236
• Lead Sponsor: Zhejiang Provincial People's Hospital

Brief Summary

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

Detailed Description

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

Study Timeline

• Status Verified: 2024-07
• Study First Submitted: 2024-07-18
• Study First Submitted QC: 2024-07-25
• Last Update Submitted: 2024-07-25
• Study Start: 2024-07-30
• Study First Posted: 2024-07-30
• Last Update Posted: 2024-07-30
• Primary Completion: 2029-07-30
• Study Completion: 2029-07-30

Recruitment & Eligibility

• Status: NOT_YET_RECRUITING
• Sex: Female
• Target Recruitment: 100000

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 & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
External test cohort	Artificial intelligence model	External test cohort is used to internally test artificial model.
Internal test cohort	Artificial intelligence model	Internal test cohort is used to internally test artificial model.
Validation cohort	Artificial intelligence model	Validation cohort is used to validate artificial model.

Primary Outcome Measures

Name	Time	Method
Area under the curve	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 Outcome Measures

Name	Time	Method
Sensitivity	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