Breast Cancer Detection Using Dogs and AI

This study is looking into a new way to find breast cancer using trained dogs and artificial intelligence (AI) by analyzing your breath. We are inviting women aged 40 and above, or those assigned female at birth, who are already scheduled for routine breast cancer screening (like a mammogram, ultrasound, or biopsy). To participate, you would simply breathe into a surgical mask to provide a breath sample. This sample will then be checked by trained dogs and an AI system. The goal is to see how well this breath test can detect breast cancer compared to standard medical tests, with results being evaluated over 24 months.

Study design
This is an observational study aiming to enroll 1204 women. It is designed to evaluate the accuracy of a breath test system.
What's involved
You would provide a breath sample by breathing into a surgical mask. This is done in conjunction with your routine breast cancer screening.
Compensation
Not stated in the trial record.
Follow-up
The study will evaluate the test performance over 24 months.

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NCT07038785

Identification of Breast Cancer in Breath Samples Using Trained Detection Dogs

Recruiting
Not specifiedAges 18+Observational
SpotitEarly
~1,204 participants
Updated 2026-05-15 on ClinicalTrials.gov
What's tested:Breath test

At a glance

Recruiting sites
6 of 6 listed sites are recruiting right now
RecruitingSuspended, closed, or not yet open
What they're measuring
Test Performance Evaluation
Measured over 24 months
Breast Cancer
6 sites across 3 states
Israel4
New Jersey1
Pennsylvania1

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  • Test Performance Evaluation24 months

    The test performance will be determined by analysis of the sensitivity and the specificity of the primary endpoint sample set, where sensitivity is the number of participants with a true positive result divided by the number of participants with a positive clinical diagnosis (positive biopsy), and specificity is the number of participants with a true negative result divided by the number of participants with a negative clinical diagnosis.