Rapid Diagnosis of Stroke-Causing Blockages Using Deep Learning Model

Rapid Diagnosis of Stroke-Causing Blockages Using Deep Learning Model

A sophisticated deep learning model helps rapidly detect stroke-causing blockages in the arteries that supply blood to the head, speeding the inset of life-saving treatment in healthcare industry.

Artificial Intelligence (AI) is changing the way healthcare industry functions. It is helping doctors diagnose patients more accurately. AI makes predictions about patients’ future health and recommends better treatment. Henceforth, healthcare organizations of all sizes, types, and specialties are becoming increasingly interested in employing AI for better patient care and improved efficiency.

Medicine is one of the fastest-growing and important application areas with unique challenges. Over a relatively short period of time, the availability of AI has exploded, leaving providers, payers and stakeholders with a variety of tools, technologies and strategies from where they can choose. Understanding how data is ingested, analyzed and returned to end-user can have a big impact on expectations for accuracy and relatability. In order to effectively choose technologies that will help develop algorithms, healthcare organisations should feel confident that they have a firm grasp on the different flavours of artificial intelligence and its use cases.

Deep learning is a good place to start with. Deep learning is a machine learning technology that teaches computers to do what comes naturally to humans. In deep learning, a computer model learns to perform classification tasks directly from images, texts or sound. Deep learning models are trained by using a large set of labelled data and neural architectures that contain many layers. In a recent study published by Radiology, a sophisticated deep learning model helps rapidly detect blockages in the arteries that supply blood to the head, potentially speeding the inset of life-saving treatment.

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