Breast Cancer ScreeningNCT07411443
AI-Enhanced Imaging in Population Breast Cancer Screening
A study testing AI-assisted screening in people with Breast Cancer Screening, run by Fudan University at 1 site in China.
Trial summary
Artificial Intelligence (AI)-assisted imaging technologies (including AI-assisted breast ultrasound and AI-assisted mammography) can effectively improve the accuracy and efficiency of breast imaging examinations, but their application in large-scale population-based breast cancer screening remains very limited. Read more about this study
- Who is it for?
- Ages 35 to 69 years, women onlyHealthy volunteers can take part
- Conditions the study is for
- Breast Cancer Screening
- What is being tested?
- AI-assisted screening
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 16,000 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2025to 31 Dec 2027
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 35 to 69 years
- Women only
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- women aged 35 to 69 years, who were attending the "Two Cancers (Breast and Cervical Cancer) Screening" project, and had no history of breast cancer, including in-situ cancer, or any other cancers in the previous five years.
Who cannot join
- have serious cardiopulmonary insufficiency, liver or kidney insufficiency, or other systemic diseases, and a life expectancy of less than five years
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site, 1 recruiting
- Recruiting
Fudan University Shanghai Cancer Center
Shanghai, Shaghai, 021
Ying Zheng, 862164172290, zhengying@fudan.edu.cn
About this study
Official title: Population-based Breast Cancer Screening Study Using AI-Assisted Imaging Technology
Artificial Intelligence (AI)-assisted imaging technologies (including AI-assisted breast ultrasound and AI-assisted mammography) can effectively improve the accuracy and efficiency of breast imaging examinations, but their application in large-scale population-based breast cancer screening remains very limited.
This project aims to improve the effectiveness and feasibility of breast cancer screening by addressing the core issues and bottlenecks in population-based breast cancer screening. We will conduct a prospective cluster-controlled screening trial in the general population, with district-based cluster grouping. The intervention group will undergo combined screening using AI-assisted ultrasound plus AI-assisted mammography, while the control group will receive conventional screening: breast ultrasound for initial screening and mammography for secondary screening.
Based on population screening practices, we will evaluate the effectiveness of AI-assisted imaging diagnostic technology in various technical aspects of actual screening and perform cost-effectiveness analyses. This study will investigate the application of AI-assisted breast imaging technology in population-based breast cancer screening, providing scientific evidence for the large-scale implementation of AI-assisted imaging technologies. Furthermore, by combining population screening practices with model simulations, we will explore multi-dimensional breast cancer screening strategies to optimize screening approaches and technologies for the Chinese population.
- Conditions
- Breast Cancer Screening
- Being tested
- AI-assisted screening
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-assisted screeningDevice
The intervention group will undergo combined screening using AI-assisted ultrasound plus AI-assisted mammography
| Study group | Role | What they receive |
|---|---|---|
| AI-assisted screening | Experimental | Not described Receives: Device: AI-assisted screening |
| Routine screening | No intervention | Not described |
What is being measured
The primary outcome is the main question the study is designed to answer.
The incidence of early-stage breast cancer over a one-year follow-up period, compared between women who underwent AI-assisted screening and those with routine screening
Early-stage breast cancer was defined as cancer confined to the breast (local) or to the breast and regional lymph nodes (locoregional). Specifically, it referred to tumors <2 cm in diameter, with no ipsilateral axillary lymph node involvement and no distant metastasis. According to the American Joint Committee on Cancer (AJCC) TNM staging system (8th edition) and the Chinese Guideline for Breast Cancer Screening and Early Diagnosis and Treatment (2021, Beijing), early-stage breast cancer encompassed stage 0 (including ductal carcinoma in situ and lobular carcinoma in situ), stage I, and stage II.
Measured: From enrollment to 1-year after the end of screening
The detection rate of suspicious breast lesions (including masses and calcifications) over a one-year follow-up period, compared between women who underwent AI-assisted ultrasound combined with AI-assisted mammography and those who received routine scree
Measured: From enrollment to 1-year after the end of screening
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted13 Feb 2026
- Study start1 Jan 2025
- Primary completion31 Dec 2027 (estimated)
- Study completion31 Dec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Breast Cancer ScreeningStudies of the same condition, open and under way.
- More trials run by Fudan UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Screening
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 16,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Fudan University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Ying Zheng, Fudan University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07411443
- Sponsor's protocol ID
- 2024AI
- Status last verified
- Jan 2026
- First submitted
- 6 Feb 2026
- Last updated
- 13 Feb 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 13 Feb 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Population-based Breast Cancer Screening Study Using AI-Assisted Imaging Technology
- ClinicalTrials.gov ID
- NCT07411443
- Acronym / sponsor ID
- 2024AI
- Conditions
- Breast Cancer Screening
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 16,000 participants (estimated)
- Lead sponsor
- Fudan UniversityUniversity, hospital or other
- Study start
- 1 Jan 2025 (actual)
- Primary completion
- 31 Dec 2027 (estimated)
- Study completion
- 31 Dec 2027 (estimated)
- First posted
- 13 Feb 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Artificial Intelligence (AI)-assisted imaging technologies (including AI-assisted breast ultrasound and AI-assisted mammography) can effectively improve the accuracy and efficiency of breast imaging examinations, but their application in large-scale population-based breast cancer screening remains very limited.
This project aims to improve the effectiveness and feasibility of breast cancer screening by addressing the core issues and bottlenecks in population-based breast cancer screening. We will conduct a prospective cluster-controlled screening trial in the general population, with district-based cluster grouping. The intervention group will undergo combined screening using AI-assisted ultrasound plus AI-assisted mammography, while the control group will receive conventional screening: breast ultrasound for initial screening and mammography for secondary screening.
Based on population screening practices, we will evaluate the effectiveness of AI-assisted imaging diagnostic technology in various technical aspects of actual screening and perform cost-effectiveness analyses. This study will investigate the application of AI-assisted breast imaging technology in population-based breast cancer screening, providing scientific evidence for the large-scale implementation of AI-assisted imaging technologies. Furthermore, by combining population screening practices with model simulations, we will explore multi-dimensional breast cancer screening strategies to optimize screening approaches and technologies for the Chinese population.
Study design
- Primary purpose
- Screening
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| AI-assisted screening | Experimental | Device: AI-assisted screening |
| Routine screening | No intervention | – |
Who may qualify
- Ages
- 35 to 69 years
- Sex
- Female only
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (1 listed)
- women aged 35 to 69 years, who were attending the "Two Cancers (Breast and Cervical Cancer) Screening" project, and had no history of breast cancer, including in-situ cancer, or any other cancers in the previous five years.
Key exclusion criteria (1 listed)
- have serious cardiopulmonary insufficiency, liver or kidney insufficiency, or other systemic diseases, and a life expectancy of less than five years
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI-assisted screeningThe intervention group will undergo combined screening using AI-assisted ultrasound plus AI-assisted mammography | Device | AI-assisted screening |
Primary outcome
The incidence of early-stage breast cancer over a one-year follow-up period, compared between women who underwent AI-assisted screening and those with routine screening
Time frame: From enrollment to 1-year after the end of screening
Early-stage breast cancer was defined as cancer confined to the breast (local) or to the breast and regional lymph nodes (locoregional). Specifically, it referred to tumors <2 cm in diameter, with no ipsilateral axillary lymph node involvement and no distant metastasis. According to the American Joint Committee on Cancer (AJCC) TNM staging system (8th edition) and the Chinese Guideline for Breast Cancer Screening and Early Diagnosis and Treatment (2021, Beijing), early-stage breast cancer encompassed stage 0 (including ductal carcinoma in situ and lobular carcinoma in situ), stage I, and stage II.
The detection rate of suspicious breast lesions (including masses and calcifications) over a one-year follow-up period, compared between women who underwent AI-assisted ultrasound combined with AI-assisted mammography and those who received routine scree
Time frame: From enrollment to 1-year after the end of screening
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 13 Feb 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing AI-assisted screening in people with Breast Cancer Screening, run by Fudan University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 13 Feb 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Ying Zheng (862164175590, j_shen@fudan.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The incidence of early-stage breast cancer over a one-year follow-up period, compared between women who underwent AI-assisted screening and those with routine screening (time frame: From enrollment to 1-year after the end of screening). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 13 Feb 2026; AzTrials last refreshed its copy on 6 Nov 2026.