![]() But many don’t appreciate how those activities differ from CRM or just how illuminating the data can be. Few CEOs would argue against the significance of customer experience or against measuring and analyzing it. Customer experience is shaped by customers’ expectations, which largely reflect previous experiences. It encompasses every aspect of an offering: customer care, advertising, packaging, features, ease of use, reliability. The study received support in part from the Alzheimer’s Drug Discovery Foundation.Anyone who has signed up for cell phone service, attempted to claim a rebate, or navigated a call center has probably suffered from a company’s apparent indifference to what should be its first concern: the customer experiences that culminate in either satisfaction or disappointment and defection.Ĭustomer experience is the subjective response customers have to direct or indirect contact with a company. In addition to Fekrat, Wisely and Richardson, study authors include Ricardo Henao, Cason B. “The retina is a window to the brain, and machine learning algorithms that leverage non-invasive and cost-effective retinal imaging to assess neurological health can be a potent tool to screen patients at scale,” said co-lead author Alexander Richardson, a student in the Eye Multimodal Imaging in Neurodegenerative Disease lab at Duke. “Having a non-invasive and less expensive means to reliably identify these patients is increasingly important, particularly as new therapies for Alzheimer’s disease may become available,” Wisely said. Ellis Wisely, M.D., assistant professor in the Department of Ophthalmology. “This is the first study to use retinal OCT and OCTA images to distinguish people with mild cognitive impairment from individuals with normal cognition,” said co-first author C. The researchers reported that the model analyzed retinal pictures and images along with quantitative data to differentiate people with normal cognition from those with a diagnosis of mild cognitive impairment with a sensitivity of 79% and specificity of 83%. The new model identifies specific features in the OCT and OCTA images that signal the presence of cognitive impairment, along with patient data such as age, sex, visual acuity, and years of education and quantitative data from the images themselves. The current study expands on that work, using machine learning techniques to detect mild cognitive impairment, which is often a precursor to Alzheimer’s. The scans – based on optical coherence tomography (OCT) and OCT angiography (OCTA) - detected structural changes in the neurosensory retina and its microvasculature among Alzheimer’s patients. “This work brings us one step closer to detecting cognitive impairment earlier before it progresses to Alzheimer’s dementia.”įekrat and colleagues previously developed a model that used retinal scans and other data to successfully identify patients with a known Alzheimer’s diagnosis. “This is particularly exciting work because we have previously been unable to differentiate mild cognitive impairment from normal cognition in previous models,” said senior author Sharon Fekrat, M.D., professor in Duke’s departments of Ophthalmology and Neurology, and associate professor in the Department of Surgery. Publishing in the journal Ophthalmology Science, the model demonstrates the potential for a non-invasive and inexpensive method of identifying the early signs of cognitive impairment that could progress to Alzheimer’s disease. The model analyzes retinal images and associated data and recognizes specific features to identify individuals with mild cognitive impairment. – A machine learning model developed by Duke Health researchers can differentiate normal cognition from mild cognitive impairment using retinal images from the eye.
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