by ISHA SALIAN
Qure.ai, a Mumbai-based startup, has been developing AI tools to detect signs of disease from lung scans since 2016. So when COVID-19 began spreading worldwide, the company raced to retool its solution to address clinicians’ urgent needs.
In use in more than two dozen countries, Qure.ai’s chest X-ray tool, qXR, was trained on 2.5 million scans to detect lung abnormalities — signs of tumors, tuberculosis and a host of other conditions.
As the first COVID-specific datasets were released by countries with early outbreaks — such as China, South Korea and Iran — the company quickly incorporated those scans, enabling qXR to mark areas of interest on a chest X-ray image and provide a COVID-19 risk score.
“Clinicians around the world are looking for tools to aid critical decisions around COVID-19 cases — decisions like when a patient should be admitted to the hospital, or be moved to the ICU, or be intubated,” said Chiranjiv Singh, chief commercial officer of Qure.ai. “Those clinical decisions are better made when they have objective data. And that’s what our AI tools can provide.”
While doctors have data like temperature readings and oxygen levels on hand, AI can help quantify the impact on a patient’s lungs — making it easier for clinicians to triage potential COVID-19 cases where there’s a shortage of testing kits, or compare multiple chest X-rays to track the progression of disease.
In recent weeks, the company deployed the COVID-19 version of its tool in around 50 sites around the world, including hospitals in the U.K., India, Italy and Mexico. Healthcare workers in Pakistan are using qXR in medical vans that actively track cases in the community.
A member of the NVIDIA Inception program, which provides resources to help startups scale faster, Qure.ai uses NVIDIA TITAN GPUs on premises, and V100 Tensor Core GPUs through Amazon Web Services for training and inference of its AI models. The startup is in the process of seeking FDA clearance for qXR, which has received the CE mark in Europe.
Capturing an Image of COVID-19
For coronavirus cases, chest X-rays are just one part of the picture — because not every case shows impact on the lungs. But due to the wide availability of X-ray machines, including portable bedside ones, they’ve quickly become the imaging modality of choice for hospitals admitting COVID-19 patients.
“Based on the literature to date, we know certain indicators of COVID-19 are visible in chest X-rays. We’re seeing what’s called ground-glass opacities and consolidation, and noticed that the virus tends to settle in both sides of the lung,” Singh said. “Our AI model applies a positive score to these factors and relevant findings, and a negative score to findings like calcifications and pleural effusion that suggest it’s not COVID.”
The qXR tool provides clinicians with one of four COVID-19 risk scores: high, medium, low or none. Within a minute, it also labels and quantifies lesions, providing an objective measurement of lung impact.
By rapidly processing chest X-ray images, qXR is helping some doctors triage patients with COVID-19 symptoms while they wait for test results. Others are using the tool to monitor disease progression by comparing multiple scans taken of the same patient over time. For ease of use, qXR integrates with radiologists’ existing workflows, including the PACS imaging system.
“Workflow integration is key, as the more you can make your AI solution invisible and smoothly embedded into the healthcare workflow, the more it’ll be adopted and used,” Singh said.
While the first version of qXR with COVID-19 analysis was trained and validated on around 11,500 scans specific to the virus, the team has been adding a couple thousand additional scans to the dataset each week, improving accuracy as more data becomes available.
Singh credits the company’s ability to pivot quickly in part to the diverse dataset of chest X-rays it’s collected over the years. In total, Qure.ai has almost 8 million studiess, spread evenly across North America, Europe, the Middle East and Asia, as well as a mix of studies taken on different equipment manufacturers and in different healthcare settings.
“The volume and variety of data helps our AI model’s accuracy,” Singh said. “You don’t want something built on perfect, clean data from a single site or country, where the moment it goes to a new environment, it fails.”
From the Cloud to Clinicians’ Hands
The Bolton NHS Foundation Trust in the U.K. and San Raffaele University Hospital in Milan, are among dozens of sites that have deployed qXR to help radiologists monitor COVID-19 disease progression in patients.
Most clients can get up and running with qXR within an hour, with deployment over the cloud. In an urgent environment like the current pandemic, this allows hospitals to move quickly, even when travel restrictions make live installations impossible. Hospital customers with on-premises data centers can choose to use their onsite compute resources for inference.
Qure.ai’s next step, Singh said, “is to get this tool in the hands of as many radiologists and other clinicians directly interacting with patients around the world as we can.”
The company has also developed a natural language processing tool, qScout, that uses a chatbot to handle regular check-ins with patients who feel they may have the virus or are recovering at home. Keeping in contact with people in an outpatient setting is an important tool to monitor symptoms, alerting healthcare workers when a patient may need to be admitted to the hospital or track patient recovery without overburdening hospital infrastructure.
It took the team just six weeks to take qScout from a concept to its first customer: the Ministry of Health in Oman.
To learn more about Qure.ai, watch the recent COMPUTE4COVID webinar session, Healthcare AI Startups Against COVID-19. Visit our COVID page to explore how other startups are using AI and accelerated computing to fight the virus.