AI-assisted blood analysis for affordable cancer screening

Breast cancer is a significant global public health concern, with 2.3 million new cases and 685,000 deaths reported worldwide in 2020. Early detection is crucial for effective treatment and improved survival rates. However, women in the Global South face significant challenges in achieving an early diagnosis, resulting in delayed diagnosis and poorer outcomes. These barriers include limited breast cancer and screening awareness, inadequate healthcare services, and poor access to mammography track programs. These challenges arise due to a lack of specialized equipment, trained personnel, and infrastructure required for mammography in low- and middle-income countries. Poverty, limited education, and inadequate healthcare facilities contribute to these challenges, particularly for women in rural areas and marginalized communities. Improving access to breast cancer screening and diagnosis in the Global South requires prioritizing cost-effective and sustainable interventions that address these challenges. Huna is committed to enhancing the effectiveness and efficiency of mammographic screening for breast cancer through its AI-powered risk stratification model. This solution helps healthcare providers optimize their mammogram capacity by prioritizing high-risk women using routine blood biomarkers and age. 

Huna's patented algorithm applied to routine blood exams accurately detects the first signs of breast cancer (up to 90% success) by considering a woman's age and non-genetic blood biomarkers. Huna targets private, and public health operators, including laboratory chains, health insurance companies, and oncology centers specialized in screening, diagnostic, and treatment. Huna's value-based remuneration model comprises capitation and pay-for-performance, providing a scalable profit model and easing the financial burden on an already constrained healthcare system.
Huna's technology comes in two forms: an API format for seamless integration with customers' databases and a web app to provide a platform for health professionals and patients to monitor female breast health continuously.
Huna's commitment to the early detection of chronic feminine diseases demonstrates its dedication to improving healthcare outcomes for women.

Huna develops artificial intelligence solutions applied to routine blood exams for early identification and large-scale monitoring of chronic diseases focusing on women's health. Given their versatility, low cost, and speed, these tools can be handy in various scenarios. Huna's AI tools can accurately predict the risk of breast cancer from routine blood exams, providing low-cost complementary methods for risk-assessment of traditional screening and diagnostic methods. Our models can detect subtle and complex changes in routine and readily available blood analytes, allowing population tracking, earlier diagnosis, and faster treatment. Huna's solution is easily integrated into regular check-ups for women over 40 years, making it cost-effective and convenient. This solution benefits Clinical Laboratories, private and public Healthcare Operators, and patients by enabling prioritization of care and referral to specific processes for cancer patients, leading to better outcomes. Huna's value-based remuneration model aligns incentives with positive outcomes, offering two pricing models for healthcare providers. 

With its mission to develop AI-powered solutions for the early detection of chronic feminine diseases, particularly breast cancer, Huna demonstrates a clear commitment to improving healthcare outcomes for women in underprivileged conditions. Huna's team takes a comprehensive approach to understand the diverse needs of the women they serve to ensure that their solution is relevant and effective. As breast cancer affects women from all backgrounds and demographics, Huna collaborates with healthcare providers to gain insights into women's challenges and pain points when undergoing mammographic screening. Moreover, Huna's team comprises members who can represent the communities they serve, including individuals with diverse backgrounds and experiences, ensuring that their solution is inclusive and considers the unique needs of all women, regardless of their background. By adopting a collaborative approach and incorporating the perspectives and insights of healthcare providers and women, Huna's team can ensure that their solution of enhancing the effectiveness and efficiency of mammographic screening for breast cancer through its AI-powered risk stratification model is meaningful and effective in improving breast cancer screening outcomes.

Huna is a HealthTech company based in Brazil that develops state-of-the-art solutions enabling early detection & large-scale monitoring of chronic diseases, particularly feminine cancers. We recently filed a second PATENT on our own Artificial Intelligence (AI) routine blood model able to create affordable and accurate digital biomarkers/panels. Some of our most significant results were published in prestigious academic journals, including early detection of Alzheimer's Disease, COVID-19 diagnosis & prognosis. The combination of AI and routine blood exams brings an opportunity for supporting diagnosis and prediction of the risks and outcomes of multiple diseases through easy-access, low-cost, and widely available laboratory tests. Our current focus is on feminine cancers, and we kickstarted this application of AI to support Breast Cancer risk assessment with a low-cost solution based on routine blood exams. We have developed a long-term R&D partnership with Fleury Group - the largest laboratory group in Brazil - to start this work. In compliance with the Research Ethics Committee and to ensure the confidentiality of all data, Fleury has provided us with more than 200,000 medical records containing laboratory tests, mammography reports, and notifications of excisional biopsies of female patients aged 40 or above who have been admitted to the institution for breast cancer screening, diagnosis, or monitoring. Using this data, we have developed a preliminary algorithm using a low-cost/routine blood panel with an area under the curve (AUC) of 88% for the detection of breast cancer. With these results, we will validate our tool's performance using real-world data supported by Barretos Cancer Hospital and A.C. Camargo Cancer Center - Brazil's most prominent public and private oncological hospitals - to prepare for large-scale market usage in the upcoming months.

Huna is currently in the process of prototyping its AI-based blood analysis tool for breast cancer risk assessment. As a Healthtech startup building and testing its product, service, or business model, Huna is not yet serving any customers with its solution. The company is focused on validating and rolling out this innovative tool, which aims to provide an affordable risk assessment solution for the early detection of breast cancer, starting with a national rollout in Brazil to help address the limited availability of mammograms in the country.

Huna is applying to Solve because we are seeking partners who can help advance our AI-based blood analysis tool for the early detection of female diseases, with a primary focus on breast cancer. We aim to overcome financial, cultural, and market barriers hindering our solution's adoption and implementation. Huna team believes that Solve's network of partners can provide valuable support, including mentorship, networking opportunities, access to resources, and non-monetary support, such as expertise and advice. Through Solve, Huna hopes to connect with like-minded individuals and organizations who share their commitment to improving healthcare outcomes for women.

1. Financial barriers: Developing and deploying AI-powered solutions for early detection of breast cancer requires substantial financial investment in research, development, and implementation. Huna may need additional funding to scale its operations, build more advanced technologies, and expand into new markets.

2. Legal barriers: Deploying healthcare technologies requires navigating complex legal frameworks and regulations. Huna may require legal support to ensure compliance with local and international regulations, protect intellectual property, and address liability concerns.

3. Cultural barriers: In some cultures, there may be stigma or taboos around discussing women's health, including breast cancer. Huna may require support in developing culturally appropriate messaging and education materials to promote awareness and encourage early detection.

4. Market barriers: Huna may face challenges in penetrating new markets due to existing competition, regulatory barriers, or limited healthcare infrastructure. Huna may require support in identifying and accessing new markets, building strategic partnerships, and developing effective marketing and distribution strategies.

Solve can help Huna overcome these barriers by connecting it with partners who can provide monetary or non-monetary support. For example, Solve can connect Huna with potential investors, technical experts, legal advisors, or marketing specialists who can provide the necessary resources and expertise to help Huna develop and scale its solutions.

Huna has set its sights on operating primarily as a white-label B2B (later B2B2C) company, providing AI-powered digital solutions that add value to existing processes and routine exams by unlocking the hidden potential of blood analytes for the early detection of female diseases. The company's primary focus is currently on validating and rolling out an affordable risk-assessment tool for early detection of breast cancer, starting with a national rollout in Brazil to fast-track access to the country's limited availability of mammograms. Using Huna's patented algorithm applied to routine exams, the company can accurately detect the first signs of breast cancer (up to 90% success) by considering a woman's age and routine blood biomarkers.

Huna's most important customers are private and public health operators, including the Public Health System in Brazil. These operators include laboratory chains, health insurance companies, and oncology centers specialized in screening, diagnostic, and treatment. These organizations are responsible for providing healthcare services to their respective populations and are likely to benefit from the increased efficiency and accuracy of breast cancer screening.

The company's value-based remuneration model consists of capitation and pay-for-performance, providing a scalable profit model and easing the financial burden on an already constrained healthcare system. Huna's fee for service model is linked to a fixed payment per patient, while pay-for-performance is based on achieving specific outcomes or targets, such as reducing the number of false positives or increasing the detection rate of breast cancer.

Overall, Huna’s business model focuses on providing a technology solution that improves the accuracy and efficiency of mammographic screening for breast cancer. The value-based remuneration model incentivizes positive outcomes for the company and its customers. Huna’s commitment to the early detection of chronic feminine diseases, including breast cancer, demonstrates our dedication to improving healthcare outcomes for all women.

At the current stage, Huna is focused on scaling up its operations in Brazil, a vast country with a  population of over 211 million people, comprising diverse groups of indigenous peoples, Europeans, Africans, and Asians, contributing to a rich and varied cultural heritage. The country has a substantial income gap, with a considerable proportion of the population living in poverty with limited access to healthcare - particularly women. Huna’s short-term goal is to improve access to public and private healthcare coverage and screening for women by providing an affordable, accessible, and high-quality solution for massive early detection of breast cancer, ultimately improving medical care cost-benefit. Meanwhile, we have started conversations with academic institutions, researchers, hospitals and laboratory chains in Latin America (with a total population of about 500mi people) facing similar challenges as those akin to the Brazilian context - severely underfunded and unequal health systems. Our goal is to foster cooperation in countries like Mexico, Colombia, Peru, Argentina and Chile (to name a few) to rollout Huna's solutions as early as mid-2024 - setting the stage to expand to other Low/Middle Income Countries facing similar restrictions, such as Sub-Saharan African countries (i.e. Nigeria, South Africa, Cameroon, Kenya) and Southeastern Asian countries (i.e. Indonesia, Vietnam, Cambodia). 

As an AI-powered health-tech company focused on improving breast cancer screening and risk assessment, Huna is measuring its progress toward its impact goals through several indicators, including:

1. The number of women screened: Huna aims to increase the number of women screened for breast cancer through its risk stratification AI model. 
2. Reduction in diagnostic time: Early diagnosis is crucial for effective breast cancer treatment and improved survival rates. Huna aims to reduce the time between diagnosis and treatment by providing healthcare services with more accurate risk assessments and prioritizing high-risk patients. 
3. Patient outcomes: Huna's value-based remuneration model aligns incentives with positive outcomes, including improved patient outcomes such as reduced mortality and increased survival rates.
4. Cost savings: Huna's solution is designed to be cost-effective and sustainable, which can lead to cost savings for healthcare providers and patients. 
5. Impact on health equity: Huna's solution addresses barriers to early breast cancer detection and diagnosis, disproportionately affecting women in low- and middle-income countries and marginalized communities.

These indicators align with several UN Sustainable Development Goals, including Goal 3: Good Health and Well-Being, Target 3.4: By 2030, reduce by one-third premature mortality from non-communicable diseases through prevention and treatment and promote mental health and well-being, and Target 3.8: Achieve universal health coverage, including financial risk protection, access to quality essential healthcare services, and access to safe, effective, quality, and affordable essential medicines and vaccines for all.

Huna's theory of change is anchored in the belief that early detection of breast cancer is critical to enhancing patient outcomes and reducing mortality rates. To that end, the solution seeks to improve access to breast cancer screening and diagnosis, focusing on addressing the barriers women in the Global South face when seeking early detection.
Huna's activities entail equipping healthcare providers with an AI-powered risk stratification model, which enables them to optimize their mammogram capacity by prioritizing high-risk women. The solution determines an individualized risk score for breast cancer by analyzing routine blood biomarkers and age.
The immediate output of this activity is identifying high-risk women who can receive appropriate care and follow-up, while the longer-term outcome is the improvement of breast cancer diagnosis and treatment, leading to better survival rates and reduced mortality.
The foundation of Huna's theory of change is supported by research that underscores the importance of early detection in enhancing breast cancer outcomes and reducing mortality. The solution also addresses specific barriers to early detection in the Global South, such as limited healthcare services and inadequate access to mammography track programs. By enhancing access to screening and diagnosis, Huna seeks to alleviate the burden of breast cancer in the Global South and ultimately improve patient outcomes.

Huna's patented algorithm applied to routine exams accurately detects the first signs of breast cancer (up to 90% success) by considering a woman's age and routine blood biomarkers. Huna targets private, and public health operators, including laboratory chains, health insurance companies, and oncology centers specialized in screening, diagnostic, and treatment. Huna's value-based remuneration model comprises capitation and pay-for-performance, providing a scalable profit model and easing the financial burden on an already constrained healthcare system. Huna's technology comes in two forms: an API format for seamless integration with customers' databases and a web app to provide a platform for health professionals and patients to monitor female breast health continuously. Huna's commitment to the early detection of chronic feminine diseases demonstrates its dedication to improving healthcare outcomes for women.

At Huna, Machine Learning models are applied to develop tests from standard blood exams to early identify and observe multiple illnesses on a large scale, taking advantage of Real World Data and Artificial Intelligence. This original methodology developed by Huna has been proven to be effective for:

Published papers and Patent

• Published in Nature Communications Medicine along with Fleury Group (one of the most respected medical and health organizations in Brazil), the identification, by applying the AI model to more than one million blood counts, of the active COVID-19 infection, mimicking the RT-PCR exam

  • https://www.nature.com/articles/s43856-022-00129-0.epdf

• Associating CBC blood analytes with urea indices and RT-PCR, they predict the severity of COVID-19 with an AUC of 92% 

  • https://www.sciencedirect.com/science/article/pii/S1386505622001496?dgcid=author

• Identification of a blood panel composed of 12 plasma proteins to predict the chance of a patient with mild cognitive impairment developing Alzheimer's disease up to four years before clinical diagnosis (with an AUC of 91%)

  • https://content.iospress.com/articles/journal-of-alzheimers-disease/jad220256

• Patent deposited, shared between Huna, Kunumi and UFMG: Privilege of Innovation. Registration number: BR1020210154110, title: "HUMAN-CENTERED PROCESS FOR THE ELABORATION OF MODELS BASED ON MACHINE LEARNING AND USES" , ARAÚJO, D. C.; VELOSO, A.A.; CARAMELLI, P. ; GOMES, K.B.; ZUIN, G.; ALVES, T. HUMAN-CENTERED PROCESS FOR DEVELOPING MODELS BASED ON MACHINE LEARNING AND USES. 2021, Brazil. Registration institution: INPI - National Institute of Industrial Property. Deposit: 08/04/2021.

Papers to be submitted 

• Machine learning data-centric approach from complete blood count test on preeclampsia screening

  • IEEE Journal of Biomedical and Health Informatics

• An artificial intelligence model for breast cancer risk assessment using routine blood exams

  • Annals of Oncology 

• Artificial intelligence model using routine blood exams for cardiotoxicity in Breast Cancer Investigation

  • AI in medicine

Huna's approach to incorporating diversity, equity, and inclusivity into their work is to challenge stereotypes and break down barriers. As a female-led company, the founder has experienced obstacles and impediments in the industry, including misconceptions about women's ability to succeed and innovate in business. Huna is working to overcome these obstacles by demonstrating competence, knowledge, and inventiveness through its use of cutting-edge technology to develop novel solutions to the problem of early detection of chronic feminine diseases.

Huna's equity mission has led to high proficiency and competence, as validated by scientific publications in high-impact journals and prestigious awards in the health industry. The company recognizes the need for more representation of women in leadership roles in the technology industry and is building a supportive network of male and female peers who share their passion for innovation and entrepreneurship.
Huna's commitment to diversity, equity, and inclusion is reflected in its efforts to challenge stereotypes, break down barriers, and build a more equitable and inclusive business environment.

Huna has set its sights on operating primarily as a white-label B2B (later B2B2C) company, providing AI-powered digital solutions that add value to existing processes and routine exams by unlocking the hidden potential of blood analytes for the early detection of female diseases. The company's primary focus is currently on validating and rolling out an affordable risk-assessment tool for early detection of breast cancer, starting with a national rollout in Brazil to fast-track access to the country's limited availability of mammograms. Using Huna's patented algorithm applied to routine exams, the company can accurately detect the first signs of breast cancer (up to 90% success) by considering a woman's age and routine blood biomarkers.

Huna's most important customers are private and public health operators, including the Public Health System in Brazil. These operators include laboratory chains, health insurance companies, and oncology centers specialized in screening, diagnostic, and treatment. These organizations are responsible for providing healthcare services to their respective populations and are likely to benefit from the increased efficiency and accuracy of breast cancer screening.

The company's value-based remuneration model consists of capitation and pay-for-performance, providing a scalable profit model and easing the financial burden on an already constrained healthcare system. Huna's fee for service model is linked to a fixed payment per patient, while pay-for-performance is based on achieving specific outcomes or targets, such as reducing the number of false positives or increasing the detection rate of breast cancer.

Overall, Huna’s business model focuses on providing a technology solution that improves the accuracy and efficiency of mammographic screening for breast cancer. The value-based remuneration model incentivizes positive outcomes for the company and its customers. Huna’s commitment to the early detection of chronic feminine diseases, including breast cancer, demonstrates our dedication to improving healthcare outcomes for all women.

Huna develops artificial intelligence solutions applied to routine blood exams for early identification and large-scale monitoring of chronic diseases focusing on women's health. Given their versatility, low cost, and speed, these tools can be handy in various scenarios. Huna's AI tools can accurately predict the risk of breast cancer from routine blood exams, providing low-cost complementary methods for risk-assessment of traditional screening and diagnostic methods. Our models can detect subtle and complex changes in routine and readily available blood analytes, allowing population tracking, earlier diagnosis, and faster treatment.

Huna's target customers include Clinical Laboratories, private Healthcare Operators (e.g. Insurers, Hospitals) and Public Health Operators. Our solution can be integrated into regular check-ups for women aged above 40 years without the need for additional tests or modifications to existing check-up protocols. After all, our AI models can analyze the results of routine blood analysis that are routinely collected for other purposes, like cardiac health evaluation, making the process convenient and cost-effective. Huna's charge for this service will be transactional, based on the number of patients tested.

Health Operators can benefit from Huna's solution by assessing the risk of breast cancer in their female population to identify patients with a higher risk of developing the disease, enabling prioritization of care and referral to specific processes for cancer patients, leading to better outcomes. Huna offers two pricing models for this service - either a fee per patient analyzed or a risk-sharing model with a low fee per patient execution and a bonus based on the model's error rate.

Overall, Huna's business model offers efficient and cost-effective solutions for the early identification and monitoring of chronic diseases through routine blood exams, benefiting patients and healthcare providers.

FINANCIALS & INVESTMENTS

(2021/2022) Investidors: Big_Bets, Kortex & Niu Ventures
Amount Raised: $ 856,834 USD 

(2022) - Winner of d’Astro ROCHE (Data Science Applied Trail Roche) - https://www.editaldastro.com.b...
Amount Raised: R$110,000 ($ 21,672.85 USD)

(2022) Google Cloud Credits - 2 years of operational support
Amount Raised: $ 201,607.90 USD (in kind)

(2022) - Approved - PIPE Fase 1/FAPESP (No. 2022/07614-3) - The São Paulo Research Foundation, FAPESP https://fapesp.br/en
Amount Raised: R$198,916.55 ($ 39,313.54 USD)

(2022) - Winner of Fleury Group Innovation Award (PIF 2022) https://fleurylab.com.br/tende...
Amount Raised: R$12,000 ($ 2,318.49 USD)

(2022) - Winner of José Eduardo Ermínio de Moraes Prize: Innovations for Life (A.C.Camargo Cancer Center & José Ermírio de Moraes Neto Family) https://accamargo.org.br/sobre...
Amount Raised: R$70,000 ($ 20,160.79 USD)

TOTAL = $ 1,141,907.13 USD