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Machine learning microscope adapts lighting to improve diagnosis Duke Engineers have developed a new type of microscope that uses a bowl studded with LED lights of various colors and lighting schemes produced by machine learning. Credit: Roarke Horstmeyer, Duke University In the initial proof-of-concept study, the microscope simultaneously developed a lighting pattern and classification system that allowed it to quickly identify red blood cells infected by the malaria parasite more accurately than trained physicians and other machine learning approaches.

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The results appear online on November 19 in the journal Biomedical Optics Express. This allows samples to be illuminated from different angles up to nearly 90 degrees with different colors, which essentially casts shadows and highlights different features of the sample depending on the pattern of LEDs ole wedel single. The researchers then fed the microscope hundreds of samples ole wedel single malaria-infected red blood cells prepared as thin smears, in which the cell bodies remain whole and are ideally spread out in a single layer on a microscope slide.

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Using a type of machine learning algorithm called a convolutional neural network, the microscope learned which features of the sample were chemnitz single party important for diagnosing malaria and how best to highlight those features. The algorithm eventually landed on a ring-shaped LED pattern of different colors coming from relatively high angles. While the resulting images are noisier than a regular microscope image, they highlight the malaria parasite in a bright spot and are correctly classified about 90 percent of the time.

  1. Machine learning microscope adapts lighting to improve diagnosis
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Trained physicians and other machine learning algorithms typically perform with about 75 percent accuracy. The other laboratory showed similar successes.

If they only had to look at a handful of cells that our microscope has already picked out in a matter of seconds, it would greatly speed up the process. For this preparation, the machine learning algorithm was successful 99 percent of the time.

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According to Horstmeyer, the improved accuracy is expected because the tested thick smears were more heavily stained than the thin smears ole wedel single exhibited higher contrast. But they also take longer to prepare, and part of the motivation behind the project is to cut down on diagnosis times in low-resource settings where trained physicians are sparse and bottlenecks are the norm.

With this initial success in hand, Horstmeyer is continuing to develop both the microscope and machine learning algorithm.

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Meanwhile, Horstmeyer is working with a different machine learning algorithm to create a version of the microscope that can adjust its LED pattern ole wedel single any specific slide it's trying to read. So instead of just dumbly taking images, it can play around with the focus and illumination to try to get a better idea of what's on the slide, just like a human would.

Biomedical Optics Express, Vol.

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