Abstract: A novel medical image generation approach synthesizes thin-cut computerized tomography (CT) images from thick-cut CT ones as inputs. First thick-cut CT images are obtained by maximizing the pixel-wise intensity of five or more than five continuous thin-cut ones after image registration. Second, the obtained thick-cut CT images are fed into a generator block which adopts an encoder-decoder architecture, where each thick-cut image is encoded into low-dimensional embedding space before decoding into multiple thin-cut ones. Third, a discriminator focuses on distinguishing original real thin-cut images from synthetic thin-cut images. An adversarial mechanism between the generator and discriminator causes the discriminator's output to provide an effective gradient update of the network parameters for the generator to increasingly improve the generator's ability to synthesize higher-quality thin-cut images and in turn promotes the discriminator's discriminating capability.

Abstract: A three dimensional classification system for recognizing cross-sectional images automatically contains a processor that executes: (1) rescaling of a plurality of cross-sectional images; and feeding the rescaled plurality of cross-sectional images into two branches; (2) feeding the rescaled plurality of cross-sectional images into a first branch for performing a plurality of convolutions on the rescaled plurality of cross-sectional images directly to learn features for distinguishing phases; (3) feeding the rescaled plurality of cross-sectional images into a second branch for reducing resolution, and then performing a plurality of convolutions on the reduced resolution plurality of cross-sectional images to learn features for distinguishing phases; and (4) concatenating convolutional output channels from the two branches to fuse global and local features, on which two fully-connected layers are stacked as a classifier to recognize cross-sectional volumetric images accurately and quickly.

Abstract: Disclosed is a computer-implemented three-dimensional image classification system (CIS) for processing and/or analyzing non-contrast computed tomography (CT) medical imaging data. The CIS is a deep neural network containing multiple Convolutional Block Attention Module (CBAM) blocks, which contain convolutional layers for feature extraction followed by CBAMs. The CBAM applies channel attention to highlight more relevant features and spatial attention to focus on more important regions. Max pooling layers operably link adjacent pairs of CBAM blocks. The output of the final CBAM block is passed to two terminal fully connected layers to generate a diagnosis. This classification system can be used to perform efficient diagnosis of hepatocellular carcinoma using solely non-contrast CT images, with diagnostic performance comparable to that of a radiologist using the current LIRADS system.

Abstract: Disclosed are systems and methods using artificial intelligence for the detection and characterization of liver cancers.

Abstract: Disclosed are microbial biomarkers and methods for accurate non-invasive diagnosis of non-alcoholic fatty liver disease (NAFLD) in subjects. The microbial biomarkers include Lactococcus lactis as well as its strains and subspecies. The microbial biomarkers include Dorea sp. 5-2. The methods include measuring abundance or copy number of the one or more microbial biomarkers in a sample from the subject. The sample may be bodily fluid, mucus, or stool. Also described are methods of treating a subject with NAFLD by administering to the subject a composition containing Lactococcus lactis and/or Dorea sp. 5-2.

Abstract: A computer-implemented system (CIS), based on the DenseNet model, for processing and/or analyzing computer tomography (CT) medical imaging input data is described. The CIS contains two or more dense blocks containing one or more modules. Within each dense block, output from preceding modules containing convolutional layers are transmitted to succeeding modules containing convolutional layers, via a gate that is controlled by a predefined or trainable threshold. The CIS also includes transition layers between the dense blocks, operably linked to pairs of consecutive dense blocks in the series configuration. The CIS can be used in a computer-implemented method for enhanced diagnoses of hepatocellular carcinoma, based analysis of one or more CT medical images.

Abstract: The present invention is a diagnostic kit and materials for: 1) the prediction of the long-term response of a chronic hepatitis B virus (HBV) carrier to treatment with nucleoside/nucleotide analog, or their combination; 2) the detection of HBV variants that exhibit reduced reactivity to antibody detection; 3) the detection of HBV variants in the precore/core region that negatively affect the course of liver disease; 4) the identification of the HBV genotype.

Abstract: The present invention is a diagnostic kit and materials for: 1) the prediction of the long-term response of a chronic hepatitis B virus (HBV) carrier to treatment with nucleoside/nucleotide analogue, or their combination; 2) the detection of HBV variants that exhibit reduced reactivity to antibody detection; 3) the detection of HBV variants in the precore/core region that negatively affect the course of liver disease; 4) the identification of the HBV genotype.