Research and Application of Ultrasonic Intelligent Diagnosis System for Ovarian Mass
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: Artificial intelligence model.
- Who it may be relevant to
- Registry conditions: Ovarian Neoplasms, Adnexal Mass. Basic parameters: No limits · Female.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Center list to be confirmed — check the primary protocol.
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Research on Automatic Detection of Ovarian Mass and Intelligent Auxiliary Diagnosis System Based on Multimodal Ultrasound Images
Overview
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.
Interventions
- Diagnostic test Artificial intelligence model
Using the artificial intelligence model to diagnosis benign, borderline, and malignant ovarian masses.
Primary outcome measures
- Area under the curve [Time frame: Through study completion, an average of 1 year]
Secondary outcome measures (1)
- Sensitivity [Time frame: Through study completion, an average of 1 year]
Eligibility criteria
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.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: Yes
Study design
- Observational model
- Other
Study locations
Center list to be confirmed — check the primary protocol.
Identifiers
NCT: NCT06528236 · KY2024053