The Blue Box

Cancer is the 2nd leading cause of death worldwide. Breast cancer diagnoses in the US have been rising from 226,000 in 2010 until now and are expected to hit 294,000 by 2030, remaining the 4th most diagnosed cancer. The most popular current screening solution is the mammogram, but an ongoing strong debate on its performance -mainly because of its low sensitivity- is leaving an open spot at the market to be filled by a pain-free, low-cost, non-irradiating new approach.

Our proposed solution, The Blue Box, enables the patient to get self-tested at home, just by downloading an app and introducing a urine sample in a box. Thanks to an AI-powered embedded algorithm, The Blue Box will not only increase women's survival but also change the way the current medicine is practiced - shifting the focus from reactive into predictive medicine.

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The American Cancer Society predicts that breast cancer will represent 30% of all cancers diagnosed in the US in 2020. However, research dedicated to it is not proportional to its incidence. Actually, the NIH recognised women as underrepresented in medical research.

This trend can be observed in the field of oncology, specifically considering current breast cancer screenings. Indeed, a study by the CDC stated that only 65% women attended it in the last 2 years, potentially resulting in 1/3 breast cancers being detected too late, and thus women having a worse prognosis and survival chance. Reasons for women skipping the mammogram-based screening are multiple: pain (41% of interviewees), difficulty of work absence and poor insurances coverage according to the Journal of Women’s Health.

Furthermore, according to the Catalan (Spain) Department of Health, only 6.45% of breast cancers diagnosed via mammogram are actually cancer. In other words, the sensitivity of the mammogram is dramatically low.

Finally, although its dose is not substantial enough to be considered harmful, biennially exposure to the mammogram increases breast cancer risk itself. 

It conclusion, there exists a need for a non-invasive, inexpensive, sensitive and in-home breast cancer screening.

It was during my bachelor studies on biomedical engineering at the University of Barcelona that I had the opportunity to take a course on what we used to call "hospital critics". In this class, students were encouraged to enter the hospital, observe and engineer solutions that might impact patient care. This is when I came to the realisation of how poorly effective the current mammogram-based breast cancer screening was. 

Moved by this frustration, I asked doctors about the requirements of a hypothetical new gold standard for breast cancer screening. Since 2017, these have been re-shaped and modified pursuing an ultimate goal: CHANGE the way we -as a society- fight breast cancer.

Instead of being cited to undergo an imaging-based screening, patients receive a Blue Box at home and are advised to download an app. The app will guide the user in collecting a urine sample in a disposable container and then introducing it inside The Blue Box. The device then automatically interacts with the sample during 30 seconds, and its embedded AI-based algorithm provides a diagnosis that is sent to the app at the user's phone: a healthy status versus a recommendation to seek further medical care.

Due to its irradiating nature, mammogram-based screenings only target a narrow population segment (typically aged 45-65) – for the diagnosis power to outweigh its cancerous radiation dose. Nevertheless, The Blue Box is aimed at all women from all ages.

The proposed scenario is that of every family owning a Blue Box, which can be used by all its female members at their desired frequency and indefinitely. Every user also has an account at The Blue App, which keeps track of her screening history throughout her whole life.

In turn, the Blue App gathers data from users worldwide, which is then used to release new software versions with enhanced the prediction algorithms that get automatically installed to every Blue Box at every home.

As a conclusion, the presented breast cancer screening solution can be performed at home, with no need of medical knowledge. Additionally, because it is highly software-based, it is considerably inexpensive to manufacture, which translates into being accessible for an extremely wide population segment, regardless of its economical status and health insurance conditions. Examplewise, a Blue Box is worth $35 and can be reused indefinitely, whilst a mammogram and an MRI cost $175 and $700 respectively per single use.

The following considerations refer to the project's intended suitability for this challenge.

• According to the WHO, the future of medical technology is that of "P4 medicine": predictive, preventative, personalised, and participatory. Coinciding with this trend, The Blue Box seeks to empower the patient and encourage her to inquire and decide on her own health (participatory) whilst prioritising prevention before cure.

• It has been long accepted by many authors that surveillance healthcare systems are more economically efficient than reactive medicine. Furthermore, our proposed inexpensive prevention technology can be implemented in an affordable primary healthcare systems, regardless of patients' economic status.

Breast cancer is one of the top 4 most diagnosed cancers worldwide. However, research on its most effective prevention strategy is still non conclusive. Whilst most authors position the mammogram as the best current option for screening, its low specificity and irradiating characteristics evidence a need for a new gold standard.

Other technologies present in the market include the magnetic resonance imaging (MRI). Whilst this technique provides good visualisation for a certain tumour morphologies, it is poorly reliable for some types of breast tumours. Furthermore, the cost of one single patient undergoing this test is of $700, considerably more expensive than the mammogram.

According to recent publications, gene testing is currently becoming a powerful alternative to image-based techniques. The gene BRCA denotes a hereditary predisposition for early onset breast cancer. However, the feasibility of applying this screening to the whole population is questionable due to its elevated price, close to $5,000.

As one can observe in the figure below, the affordability of the technology is a first classifiers of the current market approaches. Secondly, the specificity comes into consideration. Whilst traditional imaging techniques do greatly rely on a physician's experience and a subjective opinion, The Blue Box is an objective analysis whose error rate does not depend on the human factor. Instead, the decision making relies on observable numeric concentrations of certain metabolites in urine.

As early as Roman times, medicine has paid special attention to human physiological metabolites, e.g., uncontrolled diabetes was historically diagnosed by a sweet taste in urine, liver failure produced a fish-like smell... However, human metabolic studies did not meet the oncology field until April 1989 when Dr. Hywel Williams and Dr. Andres Pembroke from King’s College Hospital, London reported a case in the journal The Lancet about a Collie-Doberman owner who attended their practice.

She claimed her dog was showing increasing interest in licking a mole in her leg. The mole was then shown cancerous, saving her life. This event proved cancer producing metabolic changes in human physiology, thus altering the body's taste, texture, odour, shape... Such an event is now believed to have set the starting point in the race to find an early-stage cancer diagnosis based on cancer-related metabolites. 

As exemplified at the paragraph above, the feasibility of cancer detection via metabolic bioreactions in human samples is widely accepted. Hence, it serves as the core principle on which to build a biomedical device that is low-cost, user-friendly, non-invasive, perdurable and able to run a specific self-screening with no need of a hospital nor trainer personnel.

Furthermore, because the embedded algorithm relies on AI, the more subjects that get screened, the higher the classification rate would be and the earlier cancer could be predicted. As a conclusion, the proposed tool could potentially change the way that society fights breast cancer.

In October 2017 I started the building of the first prototype of The Blue Box as my biomedical engineering bachelor thesis at the University of Barcelona. The thesis proved the main hypothesis that “the metabolite analysis performed by The Blue Box is sufficiently significant to enable class prediction among control subjects and metastatic breast cancer patients”. The Blue Box first prototype had an overall worth of $35. 

To test the device, 90 human urine samples were collected from control subjects and breast cancer patients at a hospital in Reus, near Barcelona. Class prediction was achieved with a sensitivity of 75%.

With the ultimate goal of bringing this solution to all women in the world, in September I moved to California, which seemed the right place and time to start pursuing this dream. Here at the University of California Irvine, I pursued the master of Embedded Cyber-physical Systems, where I met my friend Billy. He soon heard about the project and got motivated to bring in his computer science background and help build the second working prototype (our master thesis).

Now we have turned into friends who not only enjoy working as a team, but are also moved by the same passion: Giving every woman in the world the possibility to prevent a deadly late stage breast cancer.

ORGANISATION ACTIVITIES

In-home pain and anxiety-free breast cancer screening device aimed at the general public

OUTPUT

Delivery of a Blue Box and a Blue App to patients. 

SHORT-TERM OUTCOME

Female patients get engaged in a novel breast cancer screening system that is available and affordable to everyone - regardless of her economical and social status.

MEDIUM-TERM OUTCOME

The patient undergoes self-screenings at its choses frequency and timing. She refers the technology to other women in her household.

The above helps shape the patient of the future - one that is empowered to own and decide on its own health.

By participating in the screening program, the user provides her metabolic data and medical condition, which is used by The Blue Box developers to enhance the AI-based classification algorithm. 

LONG-TERM OUTCOME

Breast cancer mortality decreases thanks to early prediction.

THE MISSION OF The Blue Box

As a conclusion, the proposed solution is designed to help women, a social group typically underrepresented in medical technology and research, live a full life without the fear and anxiety caused by the currently offered breast cancer screening programs.

The current readiness stage of project is prototyping. The solution has not reached the market -nor the user- yet.

In one year, it is expected for the project to be in the final prototype stage - the one intended to pass human studies and clinical trials.

According to the proposed planning, in 5 years the proposed technology will have reached the US market at least and be accessible for the user:

The female population of the US is of 165.92 million women, estimated to be placed in 119,730,128 house holds. There is an average of approximately 1 women in each household that is eligible to undergo the routine breast cancer screening program. According to the CDC, only 63% of these women do actually attend it. 

According to interviews that we have conducted to 40 women, over 80% of women with previous knowledge of The Blue Box declare themselves interested in acquiring the product.

To estimate the size of a potential market, it has been considered the hypothesis that about half of the women currently concerned about breast cancer (half of the 63%) would know of and be interested in the device. This can be approximated to 31.5% of American households. 

This translates into 37,714,999 households that might be interested in the device. Hence, this is a potential future market size for The Blue Box.

In the future, by means of social media, more households might become interested in the product.

Goal ONE

To develop a new (third) fully working prototype: 

• Software-wise:

Optimisation of the artificial intelligence parameters. To do so, a hospital has already agreed to supply urine samples so as to gather patient data.

• Hardware-wise:

Printing of the electronics in a motherboard and assembly into a 3D-printed container for increased end-customer usability.

*To be achieved during the upcoming year #1 and #2.

Goal TWO

To complete the application for a provisional patent in the US. This patent will secure the technological improvements included in the third (currently existing) prototype.

*This goal is currently being carried out

Goal THREE

To apply for more funding for the following future purposes:

• Non-provisional US patent
• Intellectual protection outside the US: Europe, Asia
• Involving a third party to handle human studies application
• Continue research on human metabolites related to breast cancer

The funds needed to complete these steps are expected to come from two main sources. On the one hand, an application to an SBIR award, from the NIH or NFS will be conducted. If awarded, it would up to 4 years of research and development of the product. The application will be submitted by early 2021 and -if awarded- funding might start by end of 2021.

On the other hand, private investors will be pitched to tackle patenting feed specifically.

*To be achieved during the upcoming years #2 to #5.

• Sociocultural barriers

When conducting market discovery interviews, it was noted that some potential end-users were initially  reluctant to trust The Blue Box due to two main reasons. Firstly, the average patient over 60 years of age tends to feel anxious when medical diagnoses are communicated via app - with absence of a medical doctor. This trend was remarkably more acute in Asian nationalities than European and US cultures.

Secondly, elderly women that have integrated the mammogram-based screening as the irrefutable standard for breast cancer screening state that they would only trust The Blue Box if formal proof was published that demonstrated that its classification rate is equal or better than that of the mammogram.

• Legal barriers

The potential future possibility of a global market implies the need to patent the technology globally. It is therefore a short-term need to protect the intellectual property in the US. Once accomplished, a later challenge will be to pitch investors so as to be able to fund European and Asian patent applications.

• Financial barriers

The current state of the project as far as funding is concerned is pursuing seed funding. Having completed the proof of concept and developed a working prototype, years 2020-2021 will be devoted to funding applications (as well as conducting further research and improving the current prototype).

• Sociocultural barriers

By means of utilising various social media platforms, it is feasible to create a virtual community that in engaged and motivated to make a good and responsible use of The Blue Box. This would make it possible to educate the population: In the case of a positive result (evidence of breast cancer), further medical advice is encouraged, but a result should not be regarded as a diagnosis until confirmed by a doctor.

Additionally, we have contemplated a possible market strategy to mitigate the impact of a diagnosis on a patient: In the event of suspect of a positive diagnosis, instead of receiving a message in the app, the patient would receive a video call from a medical doctor. This would have reviewed the case and would not only deliver the potentially stresful news but also a set of next steps to follow.

• Legal barriers

The University of California Irvine provides a various resources to help students deal with legal procedures. To date, we are currently on the process of patenting the artificial intelligence algorithm that powers The Blue Box. Both legal advisors and funding sources have been made available to our team.

• Financial barriers

Finally, we are aware of numerous financial opportunities that would provide the necessary funding to translate the current prototype into a marketed product. The MIT Solve challenge is an example of them.

I am the founder and inventor of The Blue Box. However, I have developed the first prototype as a bachelor thesis at the University of Barcelona, and the second prototype as a master thesis at the University of California Irvine (UCI). Neither of the universities hold patent royalties of the product.

The prevision for next academic year (2020-2021) is a collaboration with UCI as a research assistant. The University will provide expertise advice, guidance and a laboratory space. The resulting product will not be owned by the University.

The logistics for year 2020 - 2021 are the following:

• I (Judit) will continue to work full-time on the project. I will focus on enhancing the AI algorithm, investor pitching and regulatory affairs.
• Billy (computer science) will work part-time in continuing developing together with Judit the second Blue Box prototype, specifically system architecture (server, app, data storage..).
• Anna (Pharmacy, MBA) will work as a contractor when patenting and finance-related work peaks. 

The proposed solution is one that requires a multidisciplinary team that is able to deliver expertise in various fields.

I (Judit) am the founder and inventor of The Blue Box. I have spent the last three years dedicating my time and studies to prototyping The Blue Box. As a biomedical engineer, my contribution to the team is to act as a translator from the patient's medical requirements into an engineered ready-to-use solution. My technical responsibility within the team has been to construct, train and validate the AI algorithm to provide the diagnosis.

Billy is a computer scientist who has greatly contributed to the project in the last year. His background in app development and his experience in system architecture position him as a valuable asset for the team. He has been responsible for developing the necessary middleware to ensure an effective dataflow from The Blue Box (where it is collected) to the app. And consequently to the cloud via WiFi, where the classification algorithm is run.

Finally, Ana is a contractor that is available to join the team when funding is available and greater emphasis needs to placed in legal and finance-related issues, such as patent applications and funding search.

Finally, a team that is capable of bringing the described idea into the market is one whose combined experience includes both a close insight into the current targeted healthcare system, as well as experience in bid data management and the device connectivity.

Our team is currently partnering with the following organisations at the University of California Irvine (UCI):

• University of California Irvine @ Beall Applied Innovation ("The Cove"). The Blue Box is currently a part of their Startup Organic Growth pilot program. They provide advisors, laboratory access, office space and office hours to UCI students who are involved in translational science projects. [More info at their webiste].

• The UCI ANTrepreneur centre offers the LaunchPad program, from TechStars LA provides UCI students with an innovation advisor that guides them through the creation of a business plan and in taking the first steps into the market. During academic year 2020 - 2021 we have been LaunchPad Students. [More info at their webiste].

Starting September 2020, our team will start the following collaborations:

• Starting September 2020, my role as full-time Blue Box developer will consist on a collaboration with UCI. The university will provide advice and support and collaborate to the development of a new Blue Box prototype.

• In 2017, during the development of the first Blue Box prototype, 90 human urine samples were collected from control subjects and from metastatic breast cancer patients from a hospital. The collection protocols and patient consent forms were approved by the ethics committee of the hospital. This same hospital has agreed to initiate a new collaboration by the end of 2020. 

What is your business model? 

Key resources

• The Blue Box
• The Blue App
• Virtual community of end users created in social media

Key partners

• Medical community (to recommend and endorse The Blue Box)

Key activities

• In-home pain-free and anxiety-free breast cancer screening
• Real time results and medical assistance (if positive)

Type of intervention

• In-home screening conducted by the user
• Frequency and timing decided by the user

Channels

• Technology has a presence in social media
• Primary care medical doctors endorse the utilisation of The Blue Box

Customer segmentation

• Women over 30 who decide to substitute or complement the standard screening with our device
• Healthcare systems, who benefit from a wider screening at a lower cost

Value proposition

• Comfort: Pain-free and anxiety-free breast cancer screening
• Wider population screening: Non-irradiating and in-home screening that can therefore be applied to a large population segment 

Cost structure

• Medical professionals who deliver breast cancer diagnoses via video call
• Online platform for data storage

Revenue streams

• Less time-consuming for the patient than the traditional approach
• By providing its own patient data and current medical condition, the global AI al algorithm can be enhanced

• Phase 1: Financial dependability on grants and external funding

During the first 5 years of the project, it is expected to rely on grants and awards from external parties as well as friends and family funding. The optimal case would be that of being awarded the MIT Solve award so as to advance research during years 2020 - 2021. This would make possible to do further research on improving the AI algorithm whilst conducting an extensive collection of samples at the aforementioned hospital.

After that, a third working prototype would be ready to undergo a SBIR application to the NIH/NSF. In the optimal case of receiving it, it would support from 3 to 5 years of advanced research. After that, the product would be close to market entering stage.

• Phase 2: Financial undependability. 

Once the product is ready, a startup will be registered as The Blue Box as its central product. External venture capital pitching will help support this process. Afterwards, the product will be finally available to the end-customer, thus creating revenue.

• Phase 3: Positive revenue

If phases 1 and 2 are successfully accomplished, a third phase might proceed in which the revenue is higher than the startup expenses. Having reached this point, it will be possible to invest back in research to further enhance and modify The Blue Box.