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Multimodal NASH prognosis using 3D imaging flow cytometry and artificial intelligence to characterize liver cells.

ABSTRACT: To improve the understanding of the complex biological process underlying the development of non-alcoholic steatohepatitis (NASH), 3D imaging flow cytometry (3D-IFC) with transmission and side-scattered images were used to characterize hepatic stellate cell (HSC) and liver endothelial cell (LEC) morphology at single-cell resolution. In this study, HSC and LEC were obtained from biopsy-proven NASH subjects with early-stage NASH (F2-F3) and healthy controls. Here, we applied single-cell imaging and 3D digital reconstructions of healthy and diseased cells to analyze a spatially resolved set of morphometric cellular and texture parameters that showed regression with disease progression. By developing a customized autoencoder convolutional neural network (CNN) based on label-free cell transmission and side scattering images obtained from a 3D imaging flow cytometer, we demonstrated key regulated cell types involved in the development of NASH and cell classification performance superior to conventional machine learning methods.

SUBMITTER: Subramanian R

PROVIDER: S-EPMC9249889 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background and purposeBy means of artificial intelligence, 3D angiography is a novel postprocessing method for 3D imaging of cerebral vessels. Because 3D angiography does not require a mask run like the current standard 3D-DSA, it potentially offers a considerable reduction of the patient radiation dose. Our aim was an assessment of the diagnostic value of 3D angiography for visualization of cerebrovascular pathologies.Materials and methods3D-DSA data sets of cerebral aneurysms (n CA = 10), AVMs (n AVM = 10), and dural arteriovenous fistulas (dAVFs) (n dAVF = 10) were reconstructed using both conventional and prototype software. Corresponding reconstructions have been analyzed by 2 neuroradiologists in a consensus reading in terms of image quality, injection vessel diameters (vessel diameter [VD] 1/2), vessel geometry index (VGI = VD1/VD2), and specific qualitative/quantitative parameters of AVMs (eg, location, nidus size, feeder, associated aneurysms, drainage, Spetzler-Martin score), dAVFs (eg, fistulous point, main feeder, diameter of the main feeder, drainage), and cerebral aneurysms (location, neck, size).ResultsIn total, 60 volumes have been successfully reconstructed with equivalent image quality. The specific qualitative/quantitative assessment of 3D angiography revealed nearly complete accordance with 3D-DSA in AVMs (eg, mean nidus size3D angiography/3D-DSA= 19.9 [SD, 10.9]/20.2 [SD, 11.2] mm; r = 0.9, P = .001), dAVFs (eg, mean diameter of the main feeder3D angiography/3D-DSA= 2.04 [SD, 0.65]/2.05 [SD, 0.63] mm; r = 0.9, P = .001), and cerebral aneurysms (eg, mean size3D angiography/3D-DSA= 5.17 [SD, 3.4]/5.12 [SD, 3.3] mm; r = 0.9, P = .001). Assessment of the geometry of the injection vessel in 3D angiography data sets did not differ significantly from that of 3D-DSA (vessel geometry indexAVM: r = 0.84, P = .003; vessel geometry indexdAVF: r = 0.82, P = .003; vessel geometry indexCA: r = 0.84, P <.001).ConclusionsIn this study, the artificial intelligence-based 3D angiography was a reliable method for visualization of complex cerebrovascular pathologies and showed results comparable with those of 3D-DSA. Thus, 3D angiography is a promising postprocessing method that provides a significant reduction of the patient radiation dose.

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Multimodal NASH prognosis using 3D imaging flow cytometry and artificial intelligence to characterize liver cells.

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