Research and Application of Ultrasonic Intelligent Diagnosis System for Ovarian Mass
Trial statusNot yet recruiting
Trial phaseNot listed
Trial typeObservational
Biological sexFemale
AgeNot listed
SponsorZhejiang Provincial People's Hospital
About this trial
Research on automatic detection of ovarian mass and intelligent auxiliary diagnosis system based on multimodal ultrasound images.
Eligibility criteria
This trial accepts healthy volunteersQualifiers
During gynecological ultrasound examination, at least one patient with persistent ovarian tumor was found.
The patient underwent surgical treatment and the histopathological results.
Disqualifiers
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.
Trial population
During gynecological ultrasound examination, at least one patient with persistent ovarian tumor was found. The patient underwent surgical treatment and the histopathological results.
Trial design
Design model
Other
Time perspective
Other
Treatments tested in this trial
Artificial intelligence model
Diagnostic testUsing the artificial intelligence model to diagnosis benign, borderline, and malignant ovarian masses.
Treatment groups
100,000 Participants
are divided into 4 treatment groupsGroup A: Training cohort0 interventions
Group B: Validation cohort1 intervention
Group C: Internal test cohort1 intervention
Group D: External test cohort1 intervention
Trial outcomes
Primary outcomes
1
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 yearSecondary outcomes
1
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 yearOther outcomes
1
Specificity
Specificity refers to the ability of the test to correctly identify a negative result in an individual who does not actually have the disease. It represents the proportion of cases where the disease is negative that the test is able to detect.
Time frame
Through study completion, an average of 1 year2
Accuracy
Accuracy refers to the degree to which the results of the diagnostic test are consistent with the actual situation
Time frame
Through study completion, an average of 1 year3
Positive predicative value
Positive Predictive Value indicates the probability that a test result will be true if it is positive. In other words, it represents the proportion of individuals who are diagnosed as positive when the test result is positive who actually have the disease
Time frame
Through study completion, an average of 1 year4
Negative predictive value
Negative Predictive Value refers to the probability that if a test result is negative, the result will be true negative. It represents the proportion of individuals who are diagnosed as negative when the test results are negative that are truly free of the disease
Time frame
Through study completion, an average of 1 yearLocations
Sponsors and contacts
Click on the lead sponsor to view all of their trials.
Zhejiang Provincial People's Hospital
Lead sponsor
The Second Affiliated Hospital of Harbin Medical University
Collaborator
Sichuan provincial maternity and child health care hospital
Collaborator
The Affiliated Hospital of Qingdao University
Collaborator
Aksu First People's Hospital
Collaborator
Women's Hospital School Of Medicine Zhejiang University
Collaborator
Source ClinicalTrials.gov
This trial is not recruiting at the moment. You can still explore other options: