Clinical trial · Interventional
Establishment and Clinical Application of AI-based Multimodal Diagnosis System for Ovarian Tumors
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
Ovarian tumors are a common disease that threatens women's health. They are insidious in onset, have over ten pathological types, and exhibit diverse biological behaviors, making accurate diagnosis a key factor in clinical decision-making and improving prognosis. Introducing AI technology to establish an auxiliary diagnosis system composed of multi-dimensional clinical data, including medical imaging and tumor markers, will greatly enhance diagnosis efficiency by predicting the pathological types of common ovarian tumors. Our research group has innovatively developed an AI-based ultrasound intelligent auxiliary diagnosis software for ovarian tumors, which has been clinically validated to be effective. This project will build on this by: (1) utilizing a wealth of multi-center retrospective clinical data to combine ultrasound, MRI images, physiological, pathological, and laboratory data to form the first multi-modal ovarian tumor public dataset supporting AI tasks; (2) using convolutional neural network technology to realize multi-modal image multi-classification intelligent recognition on this dataset based on surgical pathology as the standard, and then fuse features at the level of clinical data with the intelligent recognition model to train and validate an auxiliary diagnosis model for predicting the top ten pathological types of ovarian tumors; (3) applying privacy computing and federated learning methods to conduct multi-center, prospective validation and optimization of the above model, ultimately forming a clinical auxiliary diagnosis system that can predict the pathological types of most ovarian tumors and apply it to clinical practice.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Ovarian Tumors | Ovarian Neoplasm | ALIAS | 0.90 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| An auxiliary diagnostic model for ovarian tumors | Diagnostic Test | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- type
- ACTIVE_COMPARATOR
- label
- ovarian tumors
- interventionNames
- Diagnostic Test: An auxiliary diagnostic model for ovarian tumors
Primary outcomes (2)
- measure
- Pathological diagnosis
- timeFrame
- Within one week postoperatively
- description
- After the surgical specimen is delivered to the pathology department, the pathologist conducts a gross examination, recording the size, shape, surface characteristics, and cut surface features of the specimen (e.g., the proportion of solid and cystic components of the tumor). The specimen is then processed according to standard protocols to prepare tissue sections, which are subsequently stained. Once staining is complete, the slides are independently examined under a microscope by two pathologists, who evaluate the tumor cell morphology, arrangement, and histological characteristics to determine the pathological nature (benign, borderline, or malignant) and type of the tumor. If there is a discrepancy in the diagnoses, the case is referred to a senior pathologist for review, whose final opinion shall prevail.
- measure
Eligibility
Eligibility (as posted)
- Sex
- Female
Show eligibility criteria text
Inclusion Criteria: * 1\. Continuous cases admitted for diagnosis of ovarian tumor and preparation for surgical treatment; * 2\. Complete imaging data (ultrasound or MRI) and tumor marker results within 3 months before surgery; * 3\. Voluntarily sign informed consent. Exclusion Criteria: * 1\. Patients with non-ovarian origin tumor as surgical pathology; * 2\. Repetitive cases; * 3\. Cases receiving radiotherapy and chemotherapy; * 4\. Recurrent cases; 5. Poor image quality of ovarian lesions;
References
Publications (0)
Data not yet available