Clinical trial · Observational
Multi-center Study of Deep Learning AI in Breast Mass
A Multi-center Study of Breast Mass Screening and Diagnosis Using Deep Learning AI-based on Real-time Ultrasound Examination
NCT05443672CI-TRIAL-00059460unknownClinicalTrials.gov clinicaltrialsProvenance
- 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)
This multi-center study intends to evaluate the value of the detection and differential diagnosis of breast mass using deep learning AI-based real-time ultrasound examination.
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 |
|---|---|---|---|
| Breast Neoplasms | Breast Neoplasm | ONTOLOGY_EXACT | 0.98 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Yizhun BUSMS | Device | — | UNRESOLVED |
Design
Arms and outcomes
Arms (0)
[]Primary outcomes (1)
- measure
- Diagnostic performance of breast mass using deep learning AI-based real-time ultrasound examination
- timeFrame
- 12 months
- description
- Pathology as a gold standard, to evaluate the diagnostic performance (sensitivity, specificity and accuracy)
Eligibility
Eligibility (as posted)
- Sex
- Female
Show eligibility criteria text
Inclusion Criteria: 1. Females who undergo ultrasound examination for a complaint of breast lesion; 2. The breast lesion that will obtain definite pathological diagnosis or follow-up at least two years. Exclusion Criteria: 1. The breast lesion that has received CNB or FNA; 2. The breast cancer patient who has received neoadjuvant chemotherapy.
References
Publications (0)
Data not yet available
No reference posted for this study.