Can ovarian cancer detection be improved using AI-driven diagnostic support applied to ultrasound images?

Not Applicable

• Conditions: Ovarian cancer; Cancer

• Registration Number: ISRCTN88222986
• Lead Sponsor: Karolinska Institute

Brief Summary

Not available

Detailed Description

Not available

Study Timeline

• Study Start: 2023-04-01
• Last Update Posted: 10 June 2024
• Study Completion: 2026-06-06

Recruitment & Eligibility

• Status: Ongoing
• Sex: Female
• Target Recruitment: 700

Inclusion Criteria

1. Women aged =15 years
2. Newly detected ovarian tumor
3. Capable of understanding the study information and accepts participation

Exclusion Criteria

1. Aged <15 years
2. Patients who are not capable of understanding the study information or don't accept participation

Study & Design

• Study Type: Observational
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Diagnostic accuracy in differentiating benign from malignant ovarian tumors measured by comparing the outcomes from subjective assessment, IOTA-ADNEX model scoring and previously developed deep neural network (DNN) models at one timepoint

Secondary Outcome Measures

Name	Time	Method
Accuracy in differentiating benign from malignant ovarian tumors measured by comparing the outcomes from subjective assessment, IOTA-ADNEX model scoring and previously developed DNN models - stratified by user experience (expert examiners versus non-expert examiners) at one timepoint