ChagaLens

The specific problem that the team is working to solve is the challenge of early diagnosis of Chagas disease in rural communities in Latin America. Chagas disease is a parasitic infection that affects millions of people globally, primarily in Latin America. It is a chronic disease that can lead to severe cardiac and gastrointestinal complications if left untreated. The scale of the problem is significant, with an estimated 6-7 million people infected with Chagas disease in Latin America alone, and an additional 300,000 cases in the United States due to migration from Latin America.

The factors contributing to the problem include a lack of access to diagnostic tools and healthcare services in rural communities, limited knowledge and awareness of the disease, and the long turnaround time for lab testing. These factors can delay the diagnosis of Chagas disease until the chronic stage, when treatment is less effective and costly. The team's solution aims to address these factors by providing a low-cost, non-invasive, and rapid test for Chagas disease that can be easily performed in rural communities without the need for healthcare workers. The program also includes education and awareness campaigns to empower communities and increase the willingness of individuals to get tested.

The local statistics in Brazil indicate that Chagas disease affects around 1.6 million people, with an annual cost of over $400 million to the healthcare system. In addition, the traditional vector control program used to prevent Chagas disease transmission has been limited in its effectiveness, with continued transmission occurring in some regions. Globally, Chagas disease is recognized as a neglected tropical disease, and there is a need for innovative solutions to address this health challenge.

Our solution is a low-cost and non-invasive early diagnosis program for Chagas Disease. It consists of a mobile app that utilizes a machine learning algorithm to analyze images of blood samples obtained through a MOPID lens, which attaches to a smartphone camera. The app provides a quick and accurate result, which can be shared with healthcare providers for prompt treatment.

The technology behind our solution is the combination of the MOPID lens, a smartphone camera, and a machine learning algorithm. The MOPID lens provides clear images of blood samples, and the machine learning algorithm analyzes the images to detect the presence of the parasite responsible for Chagas Disease. The app is designed to be user-friendly and accessible to people in rural communities, where traditional laboratory testing may not be available.

By providing a low-cost and non-invasive early diagnosis program, our solution aims to reduce the financial burden for healthcare systems caused by treating late-stage complications of Chagas Disease. It also provides a more efficient and convenient way for individuals to get tested, increasing the likelihood of early detection and treatment.

Our target population encompasses individuals residing in conflict and fragile settings, including those affected by climate disasters or political instability. One such community that exemplifies this demographic is the Quilombo communities in Brazil. These communities originated from Africa in the 1500s, where their members were brought to work as plantation slaves in Brazil. Despite resisting slavery for several centuries, they faced numerous challenges following the abolition of slavery in 1888. At present, approximately 3,500 Quilombo communities are scattered throughout Brazil and other Latin American regions.

Numerous studies have identified significant barriers to healthcare access for Quilombo communities, including sociocultural, racial, geographical, financial, and transportation challenges. Such obstacles discourage care-seeking health behaviors among the Quilombo people, complicating the identification of prevalent health issues. A 2018 study by Silva Martins et al. investigated the Furnas do Dionísio settlement in Jaraguari, Brazil, and discovered that 20% of the 96 surveyed households were affected by the vector transmitting Chagas disease.

Our objective is for ChagaLens to enhance access to diagnostic services for Chagas disease in marginalized communities, such as the Quilombo communities. This intervention aims to address the unique healthcare challenges faced by these populations, ultimately improving their overall well-being.

Our team leader, Stephen Chen, has traveled to Brazil and gained insights into the sociocultural driving forces within the country. Moreover, our team participated and won runner up in the Harvard Health Systems Hackathon at Harvard University, where we engaged in collaborations and discussions with numerous professors possessing extensive experience working with or living in communities facing challenges akin to those of the Quilombo communities. We plan to maintain these collaborations and actively recruit team members who are representative of Latin American communities.

In particular, we intend to visit the communities before implementing the intervention, ensuring a thorough understanding of their needs and the most effective approach for the implementation process. Identifying and involving community leaders in the design and implementation process will be crucial in incorporating their perspectives in a culturally respectful and appropriate manner. This collaborative approach aims to foster a successful and sustainable intervention that addresses the unique challenges faced by these communities.

Our program is a prototype because we have already received support and funding for developing ChagaLens into an actual program to be implemented in Northeast Brazil. We are currently in the half-year incubation period and will have significant progress for the realization of the program at the end of incubation period.

Currently, we are still in the process of developing the program an we intend to first serve a rural community with 1000 people with the pilot study, with further expansion when the results of the pilot study come back. 

At present, our team has developed the design concept for the MOPID lens, an application development plan for integration into the healthcare system, and a communications initiative aimed at our target population. However, we currently lack the financial resources necessary for implementation. Moreover, cultural barriers exist as community members have not yet been involved in the project development process. We anticipate that the connections established through Solve will equip us with the means to effectively communicate with communities in their local settings, as well as to better comprehend the geographical context in which they reside and the associated barriers.

Additionally, we aim to involve community health workers and local pharmaceutical companies in this project. We believe that funding and networking opportunities provided through MIT Solve will facilitate these collaborations, ultimately contributing to the successful implementation and sustainability of the intervention.

Our solution approaches diagnosing Chagas disease in a new way to promise low cost, low stigma, and rapid turnaround time. The solution is also relatively cheap to implement because there are currently rapid diagnosis programs targeting malaria, another parasitic disease that received much more attention. We could adapt the rapid malaria diagnosis program to Chagas by identifying the parasite in the blood smear result during the acute phase. We are also developing a machine learning algorithm to develop the imaging analysis with high sensitivity and specificity. Combining the development of both parts, we will eventually avoid the necessity of a microscope; instead, we can use the MOPID lens attached to a mobile phone to accurately identify the disease while protecting patients' privacy by allowing them to submit the result from their phones through an app.

Another important part of this innovation is the development of the surveillance program after expanding the program into more communities. Surveillance could be accomplished by mapping all cases of Chagas disease and highlighting the high-risk areas, which allows for a more invasive control program, such as a vector control program for the area through automated identification of insect vectors of Chagas Disease. Vector control programs are much more expensive than our low-cost rapid diagnosis program; Thus, identifying the area that needs vector control is essential. 

In addition, the MOPID lens and the machine learning algorithm could be modified and implemented to diagnose another parasitical disease, making this innovation sustainable. Visceral Leishmaniasis(VL) was a potential candidate, as it was a vector-borne disease affecting mainly economically vulnerable populations. In Latin America, Brazil accounted for 96% of the cases in this region; thus, VL was also a parasitic disease that required public health attention. The surveillance program introduced by our innovation could also be integrated into broader disease control programs, such as Dengue or Malaria. Our innovation will be flexible and easy to combine with other programs. 

Considering the impact on the market, one of the financial difficulties introduced by Chagas disease is that Chagas was often associated with poor health outcomes, difficulties in long-time or labor work, and even sudden death, leading to employers' fear of potential financial losses or responsibilities. For these reasons, patients with Chagas Disease were reluctant to get diagnosed and to seek medical help, which resulted in worse complications and further spread of the disease. Countering this problem, the app would return the result electronically on patients' phones, providing instructions for obtaining medications if the result returned positive. Furthermore, education provided by the app will inform community members that Chagas Disease is not scary and is easily curable in the early stage with proper medications. Thus, employers should not worry if their employees are recently diagnosed with Chagas, and employees would not fear that the positive result will lead to possible financial or physical burden. Therefore, early diagnosis combined with education will reduce the chance of getting symptomatic Chagas Disease and allows for easier employment without consideration of Chagas infections. 

For the next year, our innovation will be mainly implemented in one of the states with the highest burden of Chagas. The state we currently identified is the Pernambuco State, while the region with poverty within the state has been seen to have a higher prevalence of Chagas disease. The goal for the next year is to do a pilot study to test our innovation in one of the communities with a high burden of Chagas disease and how it could decrease the prevalence of Chagas. Thus, at the start of the pilot study, we would test everyone using golden methods of detecting Chagas. We will then implement our innovation and test the prevalence of Chagas using the golden method again at the end of the pilot study. In this way, we could approximate how much this innovation could achieve. To ensure the successful implementation of our innovation, one group of researchers would supervise the local community leaders and health workers in each program step. They would monitor the usage of the MOPID lens and blood smear test, collect data on the feasibility and accuracy of the new test and record any complaints or advice from the users. We wanted to ensure the community could operate the program with minimal staffing from the developed country.

After collecting evidence, we will show this result to our main funders: WHO, PAHO, and the Brazilian Ministry of Health. WHO currently supports eliminating Chagas Disease and has a designated World Chagas Disease Day. PAHO worked closely with WHO and has connections with local healthcare workers in Latin America. Brazilian Ministry of Health would provide funding for logistics in rural communities and delivery of MOPID lens to the community. We expect that the highest cost of this program would be the salary of the community and research staff, so we would like to minimize external staffing during the expansion stage of the program. 

For the next five years, our program will be expanding, allowing it to be much more impactful. Our goal is to make the app accessible to all populations in endemic areas of Chagas in Latin America. This will allow diagnosis of Chagas mainly during the acute phases and eliminate symptomatic Chagas disease. We are also planning to upgrade the algorithm to allow the detection of another parasitic disease, so the app can serve as a platform for detecting and surveillance for other widespread parasitic diseases. 

Achieving this goal will require little staffing as the program matures. There will only be one or two staff from the research team on call in Brazil in case of technological failure or other serious concerns. In addition, we want to train local researchers on how to operate the app and machine learning algorithm so that Brazil can operate the new program independently. 

The main goal for our intervention is to minimize time to accuratley diagnose Chagas disease in low income populations, whilst minimizing stigma and creating a surveillance system that allows to track incidence of the disease over time. Thus the imapct goal of the project is to improve health equity and outcomes amongst those with Chagas disease, aligning with theUN Sustainable Development Goals number 3 and 16. To ensure that the project remains on track for each step, SMART indicators are developed. This includes intermediate year to year indicators and per-project part indicators (including elements such as funding, app development, material development, and stakeholder negotiations). Within the first year out goal is to complete the development of ChagaLens device, application, and patient education materials, and all the necessary agreements required for ChagaLens to be implemented on site in Brazil. This will be achieved through indicators including completion of peer-reviewed and stakeholder approved patient materials, and the completion of a well functioning app. 

In our second year of the project we implement models of continuous improvement as we implement the app and device on site. This will be measured through indicators including the number of people using the app (and their demographics to ensure equitable access to the device), household surveys on stigmatization of Chagas, participants’ knowledge of the disease, and app utilization rates. Additionally, we will be holding an audit with the aim of improvement of the technology and ensuring that culturally sensitive care is delivered. 

To ensure that additional activities remain on track we will be employing specific indicators following the Stepwise model developed by J. Bryce et al (2011) to ensure that indicators account for program inputs, outputs, outcomes, and impact as well as factors such as context, service provision and coverage, utilization, and cost effectiveness. 

Because our intervention includes multiple streams of activities and impacts including patient education and destigmatization, surveillance, and diagnosis the theory of change is made up of several avenues. 

The first avenue is the diagnostic element of the model. The app uses machine learning for diagnosis and detection of the parasite in the blood sample image. Then through analysis (assuming that it is accurate) and patient characteristics the patient is able to receive a faster diagnosis (as it is on an app on their phone, rather than waiting for laboratory results) irrespective of their location (as sample image is analyzed remotely). With a faster diagnosis the patient is then able to receive treatmet quicker. As a result, in the long run the intervention leads to a decreased prevelance of Chagas disease and heart disease.

The second avenue of the program is regional surveillance. With individual diagnosis results we then are able to create a surveillance and data collection system in the region. Because Chagas disease is often underreported insufficient resources are allocated towards disease management and are employed only during later stages when patients suffer heart failure. Early diagnosis and accurate surveillance then allows regional policymakers to devote the necessary resources to disease management in the area and prioritize certain communities over other based on their needs. This then also helps to lower incidence of Chagas disease in the short run, and in the long run then reduces incidence of heart failure and creates a robust database of the disease profile in the region. Having a robust database also then allows research and innovation to take place further benefitting those with the disease.

Finally, the third avenue of the program is destigmatization. Chagas disease is highly stigmatized due to its association with low income, rural communities disincentizing individuals seek care or diagnosis, missing an early window of treatment opportunity. Furthermore, due to its stigmatized status the disease has not been a priority for national and international governing and public health bodies. As a result, destigmatization is a key element of the project in conjunction with other elements. Through the app patients receive patient education materials regardless of their diagnostic status then making them more aware of the disease and reducing some of the misconceptions and stima around the disease. Through culturally appropriate patient education and community engagement participants are able to have more open conversations and share their experiences of the disease with one another, and with their providers. This then facilitates early diagnosis and support for patients. 

To reduce the cost of diagnosis, our program aimed to reduce the need for laboratory technologies or requirements of trained lab technicians. Current rapid diagnostic tests required extraction of serum that can only be done by healthcare providers or are expensive immunochromatographic tests($US 4-7 per test). MOPID lens was our choice of candidate for achieving this goal. MOPID was developed by Texas A&M University in 2015 and could be attached to a phone's camera to take a picture of the blood smear result. It was already implemented to detect Malaria. MOPID could also be examined for usage in diagnosing Chagas disease through machine learning procedures. The adaptation of machine learning procedures in detecting Malaria to detecting Chagas was accomplished before. The expected sensitivity is 90.5%, and the specificity is 88.6% for mobile phone images with a resolution of 1 megapixel. 

            Accompanying the development of the new lens, we would develop a text-based and app-based system for testing and education purposes of Chagas disease. Because of the relatively high coverage of smartphones and the Internet in Brazil(79.72% of the Brazilian population of any age used at least one smartphone and used the smartphone(s) at least once per month), an app-based system was reasonable, while the text-based system could complement the app-based system to reach the most remote population. The community meetings would serve as the location to advocate for the new app and the at-home blood smear test distribution. In addition, it was possible to provide financial incentives such as food discounts for people who downloaded the app. After taking the blood smear images, users were going to upload two mobile phone images to the app while scientists ran these images through the machine learning algorithms. If two images came back with conflicting results, the app would return an inconclusive result, informing the users to report to a primary health care facility, which would read their blood smear samples under a professional microscope. 

            In addition, the app could serve as a proper measure to reduce the stigma associated with Chagas Disease. There were usually social consequences correlated with Chagas, as people who suffer from the disease can face work restrictions. Chagas was often associated with poor health outcomes, difficulties in long-time or labor work, and even sudden death, leading to employers' fear of potential financial losses or responsibilities. For these reasons, patients with Chagas Disease were reluctant to get diagnosed and to seek medical help, which resulted in worse complications and further spread of the disease. Countering the stigma, the app would return the result electronically on patients' phones without informing any medical providers or employers of the person. The patients would then receive instructions for obtaining medications if the result returned positive.

            Lastly, the app would serve as a platform for education and surveillance. Interactive materials would be included to allow users to learn more information about Chagas Disease. Health education has long been shown to be beneficial to community empowerment which could be positive for reducing the transmission of Chagas Disease. In addition, surveillance could be accomplished by mapping all cases of Chagas disease and highlighting the high-risk areas, which allows for a more invasive control program, such as a vector control program for the area through automated identification of insect vectors of Chagas Disease. 

Our team supports diversity, equity, and inclusivity in our research group and local staff. Our research group in the United States comprises male, female, and non-binary members. There are also members from different socioeconomic statuses. This helps us view the problem from different aspects and achieve a thorough solution targeting especially vulnerable populations. We consistently recruit new members to support us in developing technologies and implementation skills. As the project develops, we will provide a workspace that is equal, respectable, and welcoming to everyone. In addition, as we would like to improve the cultural competency of our research groups in the United States, the research team will be encouraged to visit Brazil implementation sites for at least one month during the pilot study stage to be culturally familiar with the site. The research team will also conduct training and cooperate with local researchers to empower them to run the program in Brazil eventually. The research team will also learn from advice provided by local researchers regarding barriers to reaching the project's full potential specific to the implementation sites. When choosing researchers for training, we will incorporate members from different socioeconomic levels and backgrounds to ensure all marginal populations are included. 

Our project aims to allow equitable access to rapid Chagas diagnostics to everyone in Brazil, so marginal groups in rural Brazil may require further attention. Although mobile phone coverage is over 80% of Brazil, and the main gap exists in the Amazonian region of Brazil where Chagas is not endemic, we recognize that there will be certain members that may need access to smartphones or the Internet. They will be the most vulnerable populations requiring additional effort to provide access to our project. Thus, aside from the app's development, our research team will develop a text-based system for these vulnerable populations. Research members will coordinate with community leaders about setting up a time to allow those without smartphones to use a community-shared phone to upload the blood smear images. Their mobile phone number will then be recorded and noted in our system to provide text feedback instead of app feedback. This will guarantee the privacy of the members getting tested. In addition to a text-based system, we plan to hire trustable community leaders in these vulnerable populations to identify the most marginalized population. We understand that these vulnerable populations may not trust outsiders, so our research team will contact the local outreach team to provide the same education we include in our app to these vulnerable populations. We aim to guarantee that every community member is treated equally and receives the same amount of information. 

Regarding stakeholders, Brazilian Ministry of Health(MoH) is one of the main stakeholders, as they have already been involved in projects regarding the elimination of Chagas. CUIDA Chagas was a project launched in 2021, aiming to eliminate congenital Chagas Disease. So, it could potentially be a collaborator in incorporating our program into their big project. Unitaid and the Brazilian Ministry of Health were the main funders of this program. Therefore, the Brazilian MoH would be the main driver for this program, with strong interest and high financial resources. NGOs or other international organizations, such as Unitaid, would also have a strong interest in our program, since our program would be sustainable and could be extended to other parasitical diseases, we were confident that we could attract funding from these international organizations. State and community leaders were essential in this program. We needed permission from state leaders to implement the program in a specific site for a pilot study. We also needed support from the community leaders as they were the main stakeholders in advocating our program. Community meetings led by these leaders could also be where the MOPID lens was shared between community members. Local University researchers would also be very interested in our program as our program would be the foundation of a surveillance program for Chagas disease. Also, the uploaded images could be analyzed at a local university’s computer lab to ensure a quick turnaround time for diagnosis. 

Our new program could decrease the financial burden for Brazilian MoH in reducing annual health costs caused by Chagas Disease. Treating late-stage cardiac/GI complications was expensive, while early diagnosis with antiparasitic drugs would be much cheaper. The new program also recognized the highest risk area so that the Brazilian MoH could allocate resources efficiently and provide a more intense vector control program in these areas.   Our new program fits the WHO's expectation of eliminating Chagas Disease shortly. Early diagnosis was the first step of elimination, and the innovation's low cost would also be appealing to WHO because of limited funding availability.Our new program would benefit local community members as Chagas disease was detrimental to their health. With a low-cost and rapid test available, community members would be more willing to get tested, and our goal of early diagnosis could be achieved. It would also benefit the local employers, as they would be less scared of financial losses due to disability and sudden death of their employees because of Chagas Disease. Our new program would also increase trust between community members and healthcare workers. Previously, the long turnaround time for lab testing had caused frustration in community members, as they often received back the result when it was too late for proper treatment. In addition, stigma introduced by disclosing a positive result of Chagas disease to surrounding people also raises more distrust toward healthcare workers. Our program reduced the lab turnaround time to ensure efficient treatment and privacy of the patients.

This innovation will incur several expenses. The lens is estimated to cost about $7-10, while app development can cost up to $150,000. Additional costs will be related to legal consultation, salaries for staff and logistics. It is estimated about $500,000 will be needed to implement this work, which is based on projections and comparable interventions. To fund ChagaLens, we will work with the Brazilian Ministry of Health, where we will propose that savings generated from our intervention be reinvested into the expansion and management of our innovation. Government contracts will be able to ensure that there is long-term revenue for the company, while also supplying the technology to communities that face this disease burden. In addition, we take part in various pitch competitions to be able to receive non-diluted funding for our initial costs. We are also part of the Harvard Health Systems Innovation Lab, where we will leverage their support and available funding sources. Furthermore, we will seek partnerships with various industry companies or academic institutions such as John Hopkins Bloomberg School of Public Health or Bayer Pharmaceuticals.

We have proposed this innovation at an International Hackathon hosted by the Harvard Health Systems Innovation Lab. Our team was awarded second place in the cardiovascular division in the competition. From this hackathon, we were provided a monetary prize of $3000 USD, as well as the opportunity to join the innovation lab.