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AI takes on breast cancer detection: can machines match expert radiologists?

NCT ID NCT07500428

Knowledge-focused Sponsor: Peking Union Medical College Hospital Source: ClinicalTrials.gov ↗

What the study statuses mean

This study's is highlighted.

Recruitment status, easiest to join first

Recruiting now This study
This trial is taking on new participants right now.
Not yet recruiting
Registered, but not yet taking participants.
By invitation only
Not open to general applications. Only people the study team invites can take part.
Paused
Paused for now. It may or may not start again.
Ongoing
Running, but no longer taking on new participants.
Completed
The trial has finished. Results may not be published yet.
Stopped early
Stopped early, before it reached the end. That can be for many reasons, including safety.
Cancelled
Cancelled before anyone took part.

Expanded access (not trials)

Expanded access
Not a trial. This treatment can be requested outside a study, case by case, for people who qualify.
Expanded access (paused)
Not a trial. The treatment can normally be requested outside a study, but is unavailable right now.
Expanded access (ended)
Not a trial. The treatment could once be requested outside a study, but no longer can.
Approved
The treatment has been approved, so it is available normally rather than through this programme.

When the status isn't known

Details not published
The full record has not been published yet, so there is little to show here.
Status unknown
This status has not been confirmed recently, so it may be out of date.

First seen Jun 27, 2026 · Last updated Jun 27, 2026

Summary

This study will test how well artificial intelligence (AI) can interpret breast ultrasound images using the latest BI-RADS criteria. Researchers will collect over 1,300 past ultrasound images with confirmed diagnoses and have expert radiologists annotate them. Then, various AI models will be evaluated on their accuracy in classifying breast lesions as benign or malignant. The goal is to build a standard benchmark to assess AI performance in breast imaging, which could help improve future diagnosis.

This is an AI summary of the original study and may miss details. Read our disclaimer.

Study facts

What this study's own registry entry says, in plain language.

Participants

About 1,380 people

The number the study aims to enrol. It can still change while the study runs.

Started

Mar 2026

Expected to finish

Mar 2027

An estimate. End dates often move.

Lead sponsor

Other sponsor

The registry's catch-all category, for sponsors it does not file as a company, a government agency, or a research network.

Who can take part

This study's own entry requirements. Only the study team can say for certain whether you qualify.

Who is studied

De-identified breast ultrasound images from adult patients who underwent breast ultrasound examination at Peking Union Medical College Hospital between 2018 and 2025 with subsequent pathological confirmation, supplemented by images from published, ethics-approved, open-access breast ultrasound datasets (e.g., BUSI, BrEaST).

Ages

18 to 75 years

Sex

Female participants only

Healthy volunteers

Accepted

You do not need to have the condition being studied to take part.

Show the full entry requirements

Copied word for word from the study's registry entry, so the wording is the study team's rather than ours.

Inclusion Criteria: * B-mode breast ultrasound grayscale images from the institutional PACS database or from published open-access breast ultrasound datasets with documented original institutional ethics approval * Image quality adequate for clinical diagnosis with clear visualization of the region of interest * Pathological diagnosis confirmed (for benign and malignant lesion groups), or normal breast status confirmed by a senior radiologist with \>15 years of breast ultrasound experience (for the normal group) * Complete de-identification with removal of all personally identifiable information Exclusion Criteria: * Severely degraded image quality precluding meaningful BI-RADS assessment * Duplicate images from the same patient (only the most representative image retained per lesion) * Images with residual personally identifiable information after de-identification processing * Cases with ambiguous, disputed, or unavailable pathological results * Non-B-mode ultrasound images, including elastography, contrast-enhanced ultrasound, and Doppler imaging

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Conditions

The condition(s) this trial relates to.

breast disorder breast neoplasm

As listed by the trial registrant

The condition terms exactly as the trial's registrant entered them.

Breast diseases Breast neoplasms Ultrasonography

How to take part

Only the study team decides who joins. These are the ways to reach them.

  1. The places running it

    1 site. The list below names each one and where it is.

  2. The official record

    ClinicalTrials.gov lists the study team's own contact details, including names and phone numbers. We don't republish those.

    Open the record ↗

  3. A doctor treating you

    A doctor who knows your case can contact a study site on your behalf, and can tell you whether this study is worth pursuing at all.

Contacts and locations

Locations

  • Peking Union Medical College Hospital

    RECRUITING

    Beijing, 100730, China

More trials for these conditions

Other studies related to the condition(s) this trial covers.