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Fig. 4 | Acta Neuropathologica Communications

Fig. 4

From: 3-Dimensional morphological characterization of neuroretinal microglia in Alzheimer’s disease via machine learning

Fig. 4

Retinal microglia size between AD and controls. A A schematic depicting the parameters in the column graphs B–F. B Column graphs comparing the average microglia volume (left) and convex hull volume (right) between control and AD retina using machine learning. C Column graphs comparing the average microglia area between control and AD retina using manual analysis (left) or machine learning (right). D Column graphs comparing the average microglia convex hull area between control and AD retina using manual analysis (left) or machine learning (right). E Column graphs comparing the average microglia perimeter between control and AD retina using manual analysis (left) or machine learning (right). F Column graphs comparing the average microglia convex hull perimeter between control and AD retina using manual analysis (left) or machine learning (right). Points represent individual microglia and error bars represent standard error of the mean. P-values for Mann–Whitney U test are below their respective column graphs. * means p ≤ 0.05; ** means p ≤ 0.01; **** means p ≤ 0.0001. (For manual data: Ctrl, n = 21 cells (4 subjects); AD, n = 27 cells (5 subjects)) (For machine learning data: Ctrl, n = 278 cells (4 subjects); AD, n = 268 cells (5 subjects)) (Ctrl = Control; AD = Alzheimer’s disease)

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