Release date: 2018-02-24
A cover article published by the international research team on the 22nd in the US "Cell" magazine pointed out that they have developed a new type of artificial intelligence technology that can be used to screen for common blind eye diseases and help speed up disease diagnosis.
Researchers at the Women and Children's Medical Center in Guangzhou, China, and the University of California, San Diego, used a convolutional neural network based on artificial intelligence to learn more than 200,000 optical coherence tomograms and use migration learning techniques. Let the computer learn to use the knowledge gained to solve other related problems. Convolutional neural network is a computer deep learning structure, which is a research hotspot in the field of speech analysis and image recognition.
For example, researchers have enabled artificial intelligence neural networks to learn to identify and evaluate eye anatomies such as the retina, cornea, or optic nerve more quickly and efficiently when examining whole-eye images. In addition, this new technology enables a more efficient learning experience with smaller data sets than traditional methods.
The study focused on two irreversible common blinding eye diseases: macular degeneration and diabetic retinopathy. The researchers said that both eyes can be treated if they are detected early. The results show that the new technology can determine whether the examiner needs treatment within 30 seconds, with an accuracy of over 95%.
The researchers also conducted an "occlusion test" designed to discover how artificial intelligence is thought of, occluding hundreds of areas of the image one by one, and assessing whether the accuracy is affected, thereby identifying the most relevant diagnostics in each image. Important lesion area.
Professor Zhang Kang, the research director, told Xinhua News Agency: "Machine learning is like a black box. We usually can't know exactly what happened. Through the 'occlusion test', we can find the location where the computer can get the diagnosis result in the image. Increase our trust in the diagnosis."
Source: Xinhuanet
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