Unlocking Good Health- AI enabled Tuberculosis Screening and Prison Health

Tuberculosis (TB) is a communicable disease that is a major cause of ill health and one of the leading causes of death worldwide. Until the coronavirus (COVID-19) pandemic, TB was the leading cause of death from a single infectious agent, ranking above HIV/AIDS. Globally in 2022, TB caused an estimated 1.30 million deaths  (95% UI: 1.18–1.43 million). This total includes 167,000 deaths from TB among people with HIV, which are officially classified as deaths from HIV/AIDS. COVID-related disruptions are estimated to have resulted in almost half a million excess deaths from TB in the three years 2020–2022, compared with the number that would have occurred if pre-pandemic trends had been maintained. Globally, the estimated TB incidence rate (new cases per 100 000 population per year) was 133 (95% UI: 124–143) in 2022. The net reduction from 2015 to 2022 was 8.7%, far from the WHO End TB Strategy milestone of a 50% reduction by 2025. (Global tuberculosis report 2023- WHO)

Low-resource countries are particularly vulnerable to TB and Prison Populations are disproportionality impacted. Prisons in low-resource countries have higher TB notification rates as compared to the general population, ranging from 11 to 81 times higher.

The TB burden amongst incarcerated individuals has been rising exponentially, particularly in Africa (by almost 29% from 2000-2018). TB has emerged as one of the major public health concerns across the prisons in Africa. For example, our statistics showed that the TB case notification rate in Mozambican prisons was 3000 per 100,000 (whereas the general population was 361/100,000). Increased rates of infection occur due to prisons being overcrowded, poorly ventilated, and under-resourced, with limited access to prompt TB diagnostics and treatment which makes them an ideal congregated setting for TB transmission (Cords et al., 2021; Dara et al., 2015; Kanyerere et al., 2012). 

Prison settings increase TB transmission to family and staff members when engaging with infected detainees and present a public health challenge for the general population during the period of incarceration and upon release. To accelerate the integration of the TB positive individuals in the treatment care cascade of the National Programs; accurate, quick, and timely screening especially in the absence of specialist doctors (as prisons have a significant dearth of health workforce) is required to reduce the TB burden and facilitate early access to diagnosis and treatment.        

Our proposed solution aims to address this gap by providing screening capabilities and reducing the turnaround time from screening to treatment access thereby contributing to the reduction of TB transmission and burden in these resource-constrained settings and ensuring healthcare is accessible to the vulnerable population. 

Our solution using qXR in identifying people infected with TB in prisons, and preventing disease spread is both easy to adopt and effective way. We use computer-assisted diagnostic technology to recognize over thirty lung health conditions. qXR is Qure.ai’s- FDA, CE Class IIb certified AI-based Chest X-Ray processing tool. Our solution has been trained on more than 4 million Chest X-rays in diverse settings and geographies and deployed across 80+ countries and 1800+ sites (many of which have little or no access to healthcare), and engaging key vulnerable populations representative of different demographics (e.g. prisons, nomadic population, people living with HIV, communities in rural and remote areas, etc.).

qXR aids in detecting comprehensive lung health findings on a chest X-ray in less than a minute. This reduces the chances of late diagnosis, under-diagnosis, and potential misdiagnosis and helps ensure better patient healthcare delivery. qXR has been specifically developed to augment human expertise with the power of AI.  The qXR-TB program can identify multiple signs suggesting pulmonary TB on a chest X-ray (CXR) and other findings including opacity, enlarged or calcified lymph nodes, pleural effusion, blunted CP angle, or fibrosis.

At sensitivity above 95% and 72% (69-75%) specificity qXR met the FIND’s Target Product Profile criteria for the WHO TB Triage test recommendation, supporting the evaluation studies which guided the WHO recommendation - “Among individuals aged 15 years and older in populations, computer-aided detection software programs may be used in place of human readers for interpreting digital chest X-rays for screening and triage for TB disease” (Qin et al., 2019) 

qXR has featured in 40+ peer-reviewed publications and has been used and recommended by multiple organizations across the globe including the UK NHS Trusts, the Stop-TB Partnership, FIND, PATH, etc. 

qXR is accompanied by qTrack – an end-to-end platform for TB screening, program, and case management. It tracks the end-to-end journey of a case in the TB Care Cascade to effectively monitor and establish linkage between various stages and narrow the gaps. 

Our solution qXR aims to serve the underserved incarcerated population in the high TB-burden prison settings of Africa. These prison settings are significantly under-resourced with limited access to timely diagnostics and access to treatment resulting in rapid transmission and increased morbidity and mortality due to TB.

AI-based screening of chest x-rays will help in the triaging of the TB presumptive individuals as well as asymptomatic cases in less than a minute in the absence of the specialist. This facilitates immediate isolation and sputum collection for microbiological testing thereby reducing the time from screening to access to the treatment. The identification of the people identified as TB presumptive also aids in reducing the unnecessary wastage of microbiological testing resources in congregated prison settings.

We are interested in implementing and planning the scale-up of our interventions in Malawi, CAR, Mozambique, and largely in the challenging African geography where there are significant health needs for people in places of detention. 

For instance- In Malawi, there are 33 prisons with a population of over 18,000 individuals. The key beneficiaries of our public health-promoting interventions include the carceral population, their families, and the staff who work in and administrate the prison locations and the communities of Malawi. There are currently only 16 dedicated healthcare staff who provide care across the prison system in Malawi Prison Service.  These staff cannot meet the requirements of the incarcerated population, or have access to the training and equipment required to test for many infectious diseases. Our solution can help as it involves using the qXR with mobile x-rays to access all prison locations in Malawi and to screen the total prison population and staff for TB.

Additional stakeholders that our solution serves are:   

1. Healthcare workers in the prison: The shortening of the treatment pathways will result in the utilization of the limited resources found in the prison settings thereby empowering the healthcare workers present in the prisons and giving them the tools to ensure equitable distribution of testing and pharmacological resources.  

2. Prison authorities – This solution empowers the prison authorities in ensuring the health and well-being of staff and detainees in prison settings. Our solution will reduce preventable deaths in custody and lower the rates of Tuberculosis prevalent.  

3. Larger community – When prisoners are released into the community without testing for TB, the disease spread continues unchecked. Testing staff and prisoners for TB while within the prison environment provides an ideal opportunity to test, isolate, treat, and prevent further transmission of the disease.

Qure.ai provides expertise in AI-powered medical image reading solutions for X-rays and other modalities. We work with a wide range of partners to deploy the technology and achieve maximum impact for key populations. Our solutions are deployed across 80+ countries,1800+ sites, and we impact 10 million lives yearly through our solutions by engaging with communities and vulnerable populations across the globe. We have the experience in serving in low and middle-income countries and various fragile populations across Asia and Africa including nomadic pastoralists in Nigeria, slum dwellers during the COVID-19 pandemic in Mumbai, India and prisoners in violence-affected Haiti with our partner, Health through Walls (HtW). Our preferred partner for deployment in places of detention is Health through Walls.

Health through Walls has over 20 years of experience in establishing, managing, and evaluating prison health services in developing countries. HtW is committed to providing technical assistance, training in prison health worldwide and helping to build locally owned, planned, and implemented sustainable initiatives. Their mission is to assist low-income countries in implementing sustainable improvements in their prisons, with a primary focus on the identification, prevention and management of TB, HIV and other infectious diseases. 

No other organization does the work that HtW provides. Some organizations, such as the International Committee of the Red Cross, Doctors Without Borders, International Medical Corps or Partners in Health, provide sporadic health services in prisons, but only Health through Walls has prison health as a singular mission with unique expertise.

In 2022, Qure.ai and Health through Walls undertook a pilot project of our proposed innovation to enable rapid TB triage in prisons in Haiti using Artificial Intelligence to measure the impact of our work. Mass TB screenings and active case findings were conducted in overcrowded prisons of Haiti with mobile digital X-ray units, which were equipped with qXR. Persons identified with chest radiographs suggestive of TB by qXR were immediately isolated from others to reduce the spread and taken forward for confirmatory testing and treatment initiation. The disease management software qTrack aided in maintaining electronic medical records and tracked patient journeys until treatment completion. 

We also analyzed 166 participants of which 62.6% were found to be TB presumptive by qXR, wherein 77.9% were microbiologically confirmed. The time for radiologist review of CXR was one week, while the qXR results were instantly available.

Growth Stage- We have an established solution and experience serving in low and middle-income countries and vulnerable populations across Asia and Africa including nomadic pastoralists in Nigeria, slum dwellers during the COVID-19 pandemic in Mumbai, India, and prisoners in violence-affected Haiti with our partner. 

Our partner, Health through Walls (HtW) brings in their implementation and operational expertise in Prison settings.

From our learning and experiences with the implementation in the prison of Haiti, we look forward to the mentorship from the challenge team to scale up our solution in the african geography with the specific mentorship focus on creating consortiums, partnerships, impact measurement, impact evaluation and access to investors and funding for the long term sustainability. We plan to understand the facilitators and barriers in this region and demonstrate the proof-of-concept to the prison authorities for their further support in integrating our solution for the TB screening of the inmates across all the prisons in the region. 

Mentorship and guidance from the experts in the challenge team would aid us in implementing and planning the scale-up of our intervention in Malawi, CAR, Mozambique, and largely in the challenging African geography.

Additionally, scaling up our intervention in the African geography would require consolidated efforts of a stronger consortium ensuring end-to-end TB management support which we aim to create with the challenge team’s support by accessing their network of organizations and experts. The impact measurement of our interventions with support from the challenge team would help us strengthen our effort and facilitate access to funders and investors for the scale-up, helping us reach this vulnerable population, reducing the burden of TB, and promoting Health Equity.

Suspected symptom-based screening has been a key component for active case finding for TB. But its sensitivity and specificity have been low wherein asymptomatic cases could also be missed. To improve the accuracy of the active case finding WHO recommended usage of CXRs for screening of TB. However, the utility of Chest X-rays  to diagnose TB-affected people is limited due to the unavailability of radiologists or skilled medical professionals in low-resource settings. In this scenario using an AI-based screening tool which identifies potential abnormalities on a Chest X-Ray, is an optimal way to screen for TB in under-resourced settings addressing the human resource shortage. AI-based chest X-ray screening improves the accuracy of identification of TB findings on X-rays even in asymptomatic patients. The turnaround time for chest X-ray screening is reduced to less than a minute, thereby accelerating the consequent processes in the TB care cascade.  

AI as a screening, triage, and clinical decision-making tool includes the following benefits: 

• Reduction in reporting time for reading CXRs  
• Reduction in confirmatory microbiological test consumption  
• Reduction in Turn Around Time for Diagnosis and Treatment  
• Increase in the number of Additional TB cases Detected  
• Increase in the identification of Asymptomatic Cases  
• Increase in cases identified due to clinical diagnosis  
• Reduction in overall cost for screening and diagnosis  

These benefits are layered across the spectrum of the TB care cascade and its impact creates a ripple effect on ultimately strengthening the health system and its building blocks (service delivery, health workforce, technology). 

In addition to the technical expertise, Qure.ai’s partnership with Health through Walls in delivering our solutions in Prisons with their implementation and operational expertise strengthens our solution and approach through a better understanding of the local contexts and stakeholder engagement thus integrating a bottoms-up approach in reducing the TB burden.

Theory Of Change

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References -

•Cords, O., Martinez, L., Warren, J. L., O’Marr, J. M., Walter, K. S., Cohen, T., Zheng, J., Ko, A. I., Croda, J., & Andrews, J. R. (2021). Incidence and prevalence of tuberculosis in incarcerated populations: a systematic review and meta-analysis. The Lancet. Public Health, 6(5), e300. https://doi-org.ezproxyberklee.flo.org/10.1016/S2468-... •Dara, M., Acosta, C. D., Melchers, N. V. S. V., Al-Darraji, H. A. A., Chorgoliani, D., Reyes, H., Centis, R., Sotgiu, G., D’Ambrosio, L., Chadha, S. S., & Migliori, G. B. (2015). Tuberculosis control in prisons: current situation and research gaps. International Journal of Infectious Diseases, 32, 111–117. https://doi-org.ezproxyberklee.flo.org/10.1016/J.IJID... •Dara, M., Chorgoliani, D., & De Colombani, P. (n.d.). 8. TB prevention and control care in prisons Key points. •Kanyerere, H. S., Banda, R. P., Gausi, F., Salaniponi, F. M., Harries, A. D., Mpunga, J., Banda, H. M., Munthali, C., & Ndindi, H. (2012a). Surveillance of tuberculosis in Malawian prisons. Public Health Action, 2(1), 10. https://doi-org.ezproxyberklee.flo.org/10.5588/PHA.11... •Kanyerere, H. S., Banda, R. P., Gausi, F., Salaniponi, F. M., Harries, A. D., Mpunga, J., Banda, H. M., Munthali, C., & Ndindi, H. (2012b). Surveillance of tuberculosis in Malawian prisons. Public Health Action, 2(1), 10. https://doi-org.ezproxyberklee.flo.org/10.5588/PHA.11... •Key Populations Brief: Prisoners | Stop TB Partnership. (n.d.). Retrieved April 19, 2023, from https://www.stoptb.org/key-pop... •Khan, F. A., Majidulla, A., Tavaziva, G., Nazish, A., Abidi, S. K., Benedetti, A., Menzies, D., Johnston, J. C., Khan, A. J., & Saeed, S. (2020). Chest x-ray analysis with deep learning-based software as a triage test for pulmonary tuberculosis: a prospective study of diagnostic accuracy for culture-confirmed disease. The Lancet Digital Health, 2(11), e573–e581. https://doi-org.ezproxyberklee.flo.org/10.1016/S2589-... •Soares, T. R., Dias De Oliveira, R., Liu, Y. E., Da, A., Santos, S., Pereira, P. C., Luma, S., Soares, R., Lissandra, M., De Oliveira, M., Park, C. M., Hwang, E. J., Andrews, J. R., & Croda, J. (2021). Evaluation of chest X-Ray with automated interpretation algorithms for mass tuberculosis screening in prisons. MedRxiv, 2021.12.29.21268238. https://doi-org.ezproxyberklee.flo.org/10.1101/2021.1... •Telisinghe, L., Fielding, K. L., Malden, J. L., Hanifa, Y., Churchyard, G. J., Grant, A. D., & Charalambous, S. (2014). High Tuberculosis Prevalence in a South African Prison: The Need for Routine Tuberculosis Screening. PLoS ONE, 9(1). https://doi-org.ezproxyberklee.flo.org/10.1371/JOURNA... • Velen, K., & Charalambous, S. (2021). Tuberculosis in prisons: an unintended sentence? The Lancet Public Health, 6(5), e263–e264. https://doi-org.ezproxyberklee.flo.org/10.1016/S2468-...

Our Impact Goals and Indicators would include-

• Number of prisons qXR-TB is deployed
• Number of Health professionals trained on qXR-TB
• Number of CXRs screened through qXR-TB
• Number of CXRs flagged as positive for TB
• Number of all CXRs flagged as negative for TB
• Mean Turnaround time from X-Ray generation to Sputum Test
• Number of qXR-TB presumptive scans TB positive after sputum test
• Number of TB patients screened annually
• Number of TB-positive patients identified.
• Mean overall cost to diagnose TB patients.
• Number of patients initiating TPT
• The number of patients successfully integrated into the treatment cascade.
• The number of patients who successfully completed the treatment regime.
• Experience (challenges and facilitators) of frontline workers and healthcare professionals with our AI solution.

qXR is a radiology computer-aided analysis software intended for use as an aid during chest X-ray interpretation. The solution uses artificial intelligence algorithms to analyze chest X-ray images in parallel to the ongoing standard of care image interpretation and provides a list of suspected findings on the chest X-ray. The solution trains and validates the system with large radiology data to automatically analyze and highlight suspected abnormal regions. The software application of the product works independently on cloud servers (as well as on-premises) and does not interfere with radiology IT workflow. The device can identify and localize the suspected abnormalities on chest X-rays. 

The user is presented with preview images highlighting the abnormal findings, that are meant for informational purposes only. After an automated and non-interactive processing of the x-ray images, qXR software application generates a report and bounding boxes, relevant around the region with abnormalities suspected. The solution does not alter the original medical image. It is not intended to be used as a diagnostic device, a source of medical advice or to aid in determining patient management plans. The results of the solution are intended to, be used in conjunction with other patient information & professional judgment and assist with the efficiency and accuracy of radiological image interpretation. 

Total strength at Qure.ai- 291
Full-time employees - 241
Consultants - 20
Interns - 30

8 Years

Qure.ai treats all individuals fairly and impartially, without prejudice, and does not tolerate harassment in any form. We are committed to developing a working culture that is fair and 'inclusive' -enabling all employees to make their distinctive contributions to the benefit of the business. We are also determined to ensure that we extend this same openness to our suppliers, business partners and all our customers. We expect our managers to exercise leadership in this field by discouraging prejudice and by role-modelling appropriate behavior. 

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Qure.ai's versatility and ability to quickly integrate into any client user’s existing software design or workflow, our technology/solutions can be applied to virtually any healthcare setting. Our clientele includes major metropolitan hospitals, managed care medical centers, and government departments/ministries of health. Since Qure.ai’s deep learning capabilities dramatically improve the time-to-diagnosis and aid in prioritizing cases, the technology is also well-suited to support small radiology departments in community hospitals and to assist teleradiology entities that typically manage thousands of x-ray images and scans (requiring prompt turnaround). 

Our products are constantly evolving to meet the needs of our partners reaching- vulnerable populations in hard-to-reach under-resourced- geographies and strengthening global commitments to improve health outcomes. A diverse spectrum of credible partners, a public health portfolio adept at meeting changing needs, strong deployment history/use cases, diligent customer success teams and evidence-based actions have ensured the organization is on course with their mission.  

Qure.ai utilizes a pay-per-use model allowing low-volume clinics and radiology centers in suburban, rural areas and under-resourced regions to use the technology. The company is also equipped to execute on-premises deployment. In addition, the solutions are integrated with more than 10 of the most common radiology viewing platforms, which ensures seamless integration with existing software and workflow tools already in use by Qure.ai clients.

Qure.ai has achieved financial sustainability since its inception through multiple sources:  
1. Investment: Series C funding has been secured by Qure.ai through equity-based investment.   
2. Commercial partnerships: Qure.ai’s products have been procured globally for different disease areas such as from LTE, AstraZeneca, Fujifilm etc.  
3. Grants: Qure.ai has won several grants in the past including from SBRI, India Health Fund, NHS  and STOP TB.