A urine Reader based on a proprietary eNose (electronic nose) that captures the smell of urine. It contains a chemical sensor array that detects those breast cancer biomarkers present in urine that are small enough to become volatile when the sample is heated up.
An AI algorithm that takes the activation heatmap as an input and can assert if a subject has breast cancer. This is possible thanks to certain patterns that our AI can detect within the data.
1 In 8 Women will have breast cancer at least once
Women Aged 20 – 49 do not have reliable screening methods available
81% of them want to get tested for breast cancer
Why is Now the Time for The Blue Box
The vision behind our project is the following: There already exists enough science to make breast cancer detection from a urine sample possible. However, until today, this can only be performed with expensive and big lab machinery, complex sample handling procedures, and trained personnel. A solution like this cannot be brought to all hospitals and cannot be financially accessible to all women.
Hence, we have created a low-cost, easy to use and portable device that performs as similarly as possible to the aforementioned lab machinery, and made up for the obvious decrease in robustness by implementing high-level data preprocessing algorithms and a curated classification software. Hence, by being very problem-oriented solution, our product can be made very accessible, and more breast cancers can be treated.
Our Innovation
We made standard metabolomic techniques portable and accessible.
State of the art
Science has long demonstrated the feasibility of detecting breast cancer in urine, but there is no standalone urine VOC that is univocally indicative of breast cancer
Our breakthrough
Our AI can identify breast cancer based on the presence of multiple VOCs and the proportionality between their intensities
Body fluids are typically analysed with a GC-MS (Gas Chromatography – Mass Spectrography), but such a method is unsuitable for screening
Expensive
Trained personnel and expensive facilities needed
By adding a substantial layer of data pre-processing, we can acquire high-quality data with a low-cost, easy-to-use, point-of-care device
Fast
Accessible
Non-irradiating
Comfortable
We have successfully run two pilots to validate our technology in 7 hospitals, with over 450 patients
Pilot Study 1
BB Gen 3.0 Jul 2021 – Jun 2022 5 Hospitals N=455 Patients
Pilot Study 2
BB Gen 4.2 Jul 2022 – Mar 2023 7 Hospitals N=473 Patients
The Blue Box comprises a urine reader with an electronic nose that captures the smell of urine and detects volatile breast cancer biomarkers when heated. It also includes an AI algorithm that analyzes the activation heatmaps to determine whether a person has breast cancer, utilizing pattern recognition.
In just 30 Mintues
1
Your OB-GYN specialist recommends you the test
2
You provide the urine sample
3
Your nurse practitioner analyses the sample into The Blue Box
4
The Blue Box provides the results thanks to the e-Nose and the AI algorithm
5
Your OB-GYN specialist communicates the results
In just 30 Mintues
1
Your healthcare professional recommends you the test
3
Your nurse practitioner analyses the sample into The Blue Box
5
Your OB-GYN specialist communicates the results
2
You provide the urine sample
4
The Blue Box provides the results thanks to the e-Nose and the AI algorithm
Get a Free Consultation
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“The Blue Box will offer a step forward in addressing the diagnostic challenges of breast cancer in young women, who have dense breast tissue. This novel solution holds promise in providing a reliable means of diagnosing breast cancer in this specific population, wherein conventional tools have shown suboptimal sensitivity performance.”
Míriam de la Flor, MD
Chief of Gynaecology and Obstetrics Joan XXIII University Hospital, Tarragona
“WomEC will revolutionize uterine cancer diagnosis in the world”
Javier F. Magrina,
Director Gynecologic Oncology
“From a clinical perspective, the project is expected to benefit the European community”
Frederic Amant,
Head of the gynecology oncology department, ex-chairman of the ENITEC consortium
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