Skip to content

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

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2400086850
Enrollment
Unknown
Registered
2024-07-12
Start date
2024-07-18
Completion date
Unknown
Last updated
2024-07-15

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Ovarian mass

Interventions

Index test:Ultrasonic intelligent diagnosis system for ovarian mass

Sponsors

Litao Sun
Lead Sponsor

Eligibility

Sex/Gender
Female
Age
14 Years to 80 Years

Inclusion criteria

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

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.

Design outcomes

Primary

MeasureTime frame
Area under the curve;Sensitivity;Specificity;Accuracy;Positive predicative value;Negative predictive value,;

Countries

China

Contacts

Public ContactLitao Sun

Zhejiang Provincial People's Hospital

sunlitao@hmc.edu.cn+86 151 5811 0363

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026