A wearable device that is designed to detect abnormalities in breast thermal patterns and tissue elasticity, which can be strong indicators of early stage breast cancer. It consists of two biosensing patches that the user can place on her own brassier. 

The patches collect data that is sent to the user’s phone or tablet through blue-tooth technology, and our Artificial Intelligence algorithms produce a risk assessment within minutes. 

 Thermal Abnormalities

As cancer cells multiply, blood vessels grow around them in a process known as angiogenesis, in order to provide nutrients that allow the tumor to grow. These blood vessel formations are distinguishable from those naturally occurring in the body, and hence breast cancer risk can be assessed by detecting the thermal patterns associated with them.

Tissue Elasticity

Benign and malignant tissues have different elastic properties and hence breast cancer risk can be assessed by quantifying tissue elasticity. Our tactile sensors produce smooth vibrations that excite the breast tissue surrounding the mammary glands, generating elasticity data that our artificial intelligence algorithms study to determine the presence of abnormal masses.

Mode Of Use

The wearable device can be used in the commodity of the user’s home. The user just has to place the device, follow simple instructions on her phone or tablet, and in less than an hour she will have results from her monthly examination. During this time, the user is free to continue with her daily routine as long as she doesn’t engage in intense physical activity and is not heavily exposed to sunlight.

The Oracle Behind The Bra

The device uses state of the art Artificial Intelligence algorithms in order to provide the most reliable results possible with the information collected through its sensors. Our algorithms are in constant refinement, and they learn from each new user they have access to. 

We have fed our algorithms with breast data from about 150 women, and we have obtained a 89% sensitivity (percentage of cancer cases detected as such).

We are confident that our results will only continue to improve as our trials provide more cases for our algorithms to learn from and as our engineers and scientists continue to refine our mathematical models.

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