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Deep learning-based algorithm can potentially improve diagnosis of eye and brain diseases
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May 4, 2021
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<img src="https://www.nei.nih.gov/sites/default/files/styles/small/public/2021-05/WeakGCSeg-creditFarsiuDukeU.jpg?itok=5T6pyh6M" alt="Slice of OCT image hovers over 3D segmentation of retinal cells" width="720" height="720">
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<p>The graphic shows an image generated by AO-OCT (top), and the result of WeakGCSeg algorithms to identify and trace the shapes of the ganglion cells in the eye (bottom). Image credit: Sina Farsiu, Duke University</p>
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<p>A new combination of optical coherence tomography (OCT), adaptive optics and deep neural networks should enable better diagnosis and monitoring for neuron-damaging eye and brain diseases like glaucoma.</p><p>Biomedical engineers at Duke University led a multi-institution consortium to develop the process, which easily and precisely tracks changes in the number and shape of retinal ganglion cells in the eye.</p><p>In their new paper, Sina Farsiu and Somayyeh Soltanian-Zadeh, a postdoctoral researcher in Farsiu’s lab, develop a highly adaptive and easy-to-train deep learning-based algorithm that is the first to identify and trace the shapes of ganglion cells from adaptive optics OCT scans.</p>
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