Qure.ai equipped to resist the Grim Reaper? : Qure.ai takes on the Diagnostic gaps with its Deep Learning Model.
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How does Qure.ai's qER work?
The qXR was launched in 2017, having been trained on millions of scans and subsequently earning a CE certification (a regulatory approval mark that shows a product meets European Union health and safety standards), WHO endorsement for TB triage, and FDA clearance, breaking ground for AI tools validated by real-world medical bodies.qXR, as mentioned prior, is an AI-powered chest X-ray interpretation tool curated by Qure.ai. It inculcates deep learning to analyze chest radiographs and detect abnormalities such as tuberculosis, pneumonia (not excluding COVID-19), lung nodules, fibrosis, and more.It functions by processing the image first, wherein the model analyzes the X-ray, cleans it, and then standardizes it. It then adjusts the brightness and contrast so that images from different machines are easily comparable. The model then resizes the images and crops them so they focus on the chest region. The AI model also removes artifacts such as text labels, ECG leads, or any noise that may interfere with diagnosis.qXR also uses convolutional neural networks (CNN), a type of deep learning model that is able to automatically identify patterns in the chest X-ray that are linked to the diseases. This process is referred to as 'feature extraction.' CNN learns features such as opacities (the cloudy areas in the lungs), cavities (hollow areas commonly found in TB patients), consolidation (fluid-filled lung sections), and pleural effusion (fluid around the lungs).Upon having these features extracted, the deep learning model interprets if the image shows any sign of a disease. It runs the image through pre-trained classification models to garner a numerically derived result, for example, '92% chance of tuberculosis.'The platform, to make the results easily explainable, overlays a heatmap on the X-ray to show which areas are the most likely to be severely affected and which are not. The heatmap tool used for this purpose is called a 'saliency map.' This visual tool highlights regions that the AI focused on, garnering results that claim 'AI saw pneumonia in this area,' proceeding to paint that area in red.The last step involved in this procedure is having to generate a report. The AI model is enabled to automatically generate a report in the appropriate format.In 2018, Qure fulfilled its ambition to extend into CT-based diagnostics, subsequently introducing qER for urgent cases that involve hemorrhages, strokes, and fractures, scanning and triaging results in under ten seconds, proving itself to be an essential tool when entering the time-critical window for stroke intervention. As COVID-19 struck the world silent, Qure's qXR was used for early detection of pneumonia and viral lung patterns, helping fill the accessibility-related gaps in RT-PCR testing, particularly in resource-limited settings.The procedure begins with image acquisition and processing, where a non-contrast head CT scan is captured and the images are standardized using preprocessing, much like how qXR does it.Unlike chest X-rays, which are 2D, CT scans are 3D data volumes wherein multiple image slices are placed atop one another. Therefore, in contrast to qXR, qER uses 3D CNN to process the scan in full. The model then identifies patterns across the multiple layers, aiming to spot signs of bleeding within the brain tissue (intraparenchymal hemorrhage), bleeding between the brain layers (subdural/epidural hematoma), and bleeding in the brain's surface vessels (subarachnoid hemorrhage and cranial fractures).Further, there exists the process of abnormality detection, where each abnormality is scored based on probability, localization (highlighting the exact region in the brain), and urgency (how quickly a radiologist or ER team should be intervening). The abnormal areas, upon being identified, are then highlighted using the previously mentioned saliency maps, giving radiologists visual cues about what the AI saw and where.Lastly, qER detects the critical issues and instantly alerts the radiologist or the ER team for momentary intervention.With its product suite proving clinical value in both routine screenings and emergency care, Qure.ai 's technological strides soon began translating into significant investor interest and global traction.The company's financial backing has fueled its expansion and also global outreach. In September 2024, Qure.ai closed a $65 million Series D round to deepen its presence in the United States and advance AI foundation models and acquisitions. As of November 2024, it had raised $125 million, with the company now worth around $264 million in the market, backed by major investors. The company portrays strong growth wherein its revenues are skyrocketing at 60–70% annually, and Qure.ai now serves approximately 15 million patients each year, with around 25% of its revenue coming from the US market.Eventually, Qure.ai climbed up the pedestal in the industries and geographies. Today, its solutions are deployed at over 3,100 sites in more than 90 countries, including NHS hospitals in the UK, major centers in the US, mobile X-ray vans in the Philippines, and even equine ambulances in Lesotho, supporting around 25 million people worldwide. Investors believe this global footprint highlights the company's relevance across markets. 'Qure is making quality healthcare accessible in the US and Europe as well as globally in Asia, Africa, and Latin America," added Dev Khare, Partner, Lightspeed.The startup has also made clinical partnerships with industry giants and public health services, embedding Qure.ai tools into public health programs, screening drives, and pharmaceutical trials.Looking ahead, Qure.ai is working toward profitability in the next financial year, with plans for an IPO within two years, throwing light onto its ambition to bring AI into mainstream healthcare diagnostics. As global healthcare costs rise, diagnostic backlogs grow, and workforce limitations persist, Qure.ai's product line featuring qXR, qER, stroke triage tools, and patient management apps is becoming a pivotal offering in modern medicine.This shift is especially relevant amid widening diagnostic gaps and rising demand for radiology services across both developed and underdeveloped regions. 'AI helps to overcome healthcare bottlenecks, from imaging reporting backlogs to workforce shortages, not only in Western societies like the US and Europe but also in developing nations,' said Prashant Warier, co-founder and CEO of Qure.ai.To conclude, Qure.ai stands not just as a startup but as a healthcare infrastructure builder: democratizing radiological expertise, enabling early diagnosis of life-threatening diseases, and empowering clinicians worldwide. Its trajectory from AI-powered X-ray and CT tools to near-term IPO ambitions marks it as one of India's most significant contributions to global healthcare innovation.
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