Publicação: 4 de outubro de 2022
Tuberculosis (TB) is the second deadliest infectious killer, after COVID-19, which claimed 1.5 million lives in 2020 but is now largely under control. Meanwhile, multi-drug-resistant TB remains a public health crisis and a health security threat. The World Health Organization confirms that the COVID-19 pandemic could start to unravel years of progress in the fight against tuberculosis. This is largely a result of disruption to access to TB services and a drop in resources, which has led to a fall in the detection of new cases. Due to restricted access to diagnostics and lockdowns imposed to contain the COVID-19 pandemic, 4.1 million cases went undiagnosed. India was the worst (41%) with Indonesia (14%) and the Philippines (12%) following next.
Viewed against the milestone of a 35% reduction in TB deaths by 2020, detailed in The End TB Strategy the global reduction in the corresponding time period has only been 9.2%.
To achieve the targets set forth in The End TB Strategy, patients must be put at the heart of service delivery, and early diagnosis and prevention is the first step. A robust infrastructure for testing and an adequate and trained workforce are the essential tenets needed to achieve the same. The 2021 Global TB report, however, finds that spending on TB diagnostic, treatment and prevention services fell from $5.8 billion to $5.3 billion, which is less than half of the global target for fully funding the tuberculosis response of $13 billion annually by 2022.
The current situation, along with the importance of data, has triggered a rising awareness and acceptance of the need for evolution in our approach to healthcare workflows. This acknowledgement has been made easier by the rapid strides taken by machine learning and artificial intelligence (AI) driven solutions specifically designed to address medical needs.
AI’s role in diagnostics is growing rapidly. The broad areas in which it can assist hospitals and clinicians include efficient and accurate clinical decision-making, medical image recognition, workflow streamlining via the automation of repetitive tasks, relieving administrative burdens and treatment management. In particular, the field of radiology has been swift to embrace the use of AI solutions. This is because the field is data-driven and diagnosis depends on visual confirmation and interpretation of chest X-rays by trained radiologists. This is where a significant challenge lies.
The global shortage of radiologists is one of healthcare’s unspoken predicaments. More than two-thirds (5.2 billion) of the 7.9 billion people on earth do not have access to one. The shortage of this skillset is a key factor behind the exacerbating issues in lung healthcare and it is an area that AI solutions can impact by reducing the pressure on time and resource-strapped medical imaging professionals, assisting them to process considerable volumes of imaging data, triage critical cases and create reports.
There are various organizations developing AI solutions for medical imaging. One of them is Qure.ai, which has obtained FDA/CE clearances to highlight and prioritise abnormalities in chest X-rays. Let’s look at an example of how Qure.ai’s solution was deployed and contributed to alleviating issues and enhancing existing tuberculosis systems.