DeepTek.ai's CXR Analyser Gains FDA Approval for Chest X-ray AI Solution

Pune-based DeepTek.ai, an AI-radiology company, has received US Food and Drug Administration (FDA) clearance for its Chest X-ray AI solution, the CXR Analyser. 

The advanced technology utilises deep learning algorithms to detect anomalies in chest X-rays, offering automated analysis and precise identification of suspicious areas to aid clinicians in accurate interpretations.

Commenting on the same, Dr Amit Kharat, cofounder, DeepTek.ai, stated, "DeepTek CXR Analyser v1.0 is a game-changer, reducing radiologist workload by an impressive 30'“50 percent. With FDA clearance, we're accessing the vast US healthcare market and impacting lives globally."

The CXR Analyser comprehensively covers the chest area, providing analysis for a wide spectrum of lung, pleural, and cardiac pathologies and foreign bodies/hardware. It's compatible with various X-ray machines, from hospital-based to ultra-portable units, showcasing its versatility and effectiveness.

By detecting and locating suspicious lesions in chest X-rays, this AI model expedites decision-making crucial for diagnosing conditions such as lung infections, cancer, and chronic lung diseases. 

Given the extensive usage of chest X-rays, with 1.5 billion conducted annually out of 3.5 billion total X-rays, precise interpretation becomes imperative. The shortage of radiologists further underscores the role of AI in addressing this challenge.

Established in 2017, DeepTek.ai uses AI technology to develop an advanced decision support system tailored for radiologists. The company's solutions are designed to alleviate radiologists' workload and expedite diagnosis. 

In a significant development in June, DeepTek.ai secured  India's first US-FDA clearance for their AI-powered Augmento platform, further solidifying its commitment to transforming radiology through artificial intelligence.

Similarly, in September, Qure.ai achieved FDA clearance for its AI-powered chest X-ray solution, qXR-CTR. This advanced technology employs a deep-learning-based computer vision algorithm designed to automate the assessment of cardiothoracic ratio (CTR) on chest radiographs, enhancing efficiency and accuracy for physicians across various healthcare settings.