Google DeepMind uses artificial intelligence to analyze eye diseases | mashdigi

In addition to the AlphaGo artificial intelligence system launched in March this yearDefeated Korean Go champion Lee SedolAt the same time, AlphaGo was challenged to play games. The Google DeepMind team is currently applying artificial intelligence technology to eye disease analysis to help the medical system detect eye diseases in patients more quickly and achieve early prevention effects.

The Google DeepMind team and the UK National Health Service (NHS) at the famous Moorfields Eye Hospital in LondonCooperation projectIn this collaboration, a machine learning-based artificial intelligence system analyzed tomography imaging data from over 160 million eye patients, identifying potential signs of various eye diseases and enabling early prevention. The patient data used in this collaboration was provided anonymously, thus avoiding any medical privacy concerns.

This technology application primarily analyzes data related to wet age-related macular degeneration and diabetic retinopathy, with the goal of preventing 98% of vision loss. Historically, diabetes-related blindness accounts for approximately 25% of the general population. By using artificial intelligence to analyze large amounts of physiological data and tomographic images, this potential risk can be identified early and addressed through appropriate treatment.

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The collaboration is between the DeepMind team and the UK National Health ServiceFirst application of artificial intelligence systems in medical analysis researchBefore that, AlphaGo successfully defeated the Korean Go King Lee Sedol. Later, AlphaGo took a step further and challenged electronic game projects, and played them through the thinking mode of the human brain.

Judging from the current development of artificial intelligence technology, most of them use large amounts of data to compare and analyze the best suitable projects, and further cooperate with neural network systems to simulate human brain memory and response patterns, so as to achieve a learning model that is close to human. And through computer computing efficiency, the best answer can be found in the shortest time. At this stage, applications include data analysis, system optimization, or assisting in logical operations. At the same time, many large and small services have also begun to introduce artificial intelligence systems to increase service efficiency.