Neighborhood-pixels algorithm combined with Sono-CT in the diagnosis of cirrhosis: an experimental study

Diagnosis of Liver Cirrhosis and Liver Fibrosis by Artificial Intelligence Algorithm-Based Multislice Spiral Computed Tomography

This research was aimed at investigating the artificial intelligence (AI) segmentation algorithm-based multislice spiral computed tomography (MSCT) in the diagnosis of liver cirrhosis and liver fibrosis. Besides, it was aimed at providing new methods for the diagnosis of liver cirrhosis and liver fibrosis. All patients were divided into the control group, mild liver fibrosis group, and significant liver fibrosis group. A total of 112 patients were included, with 40 cases in the mild liver fibrosis group, 48 cases in the significant liver fibrosis group, and 24 cases who underwent computed tomography (CT) examination in the control group. In the research, deconvolution algorithm of AI segmentation algorithm was adopted to process the images. The average hepatic arterial fraction (HAF) values of patients in the control group, mild liver fibrosis group, and severe liver fibrosis group were 17.59 ± 10.03%, 18.23 ± 5.57%, and 20.98 ± 6.63%, respectively. The average MTT values of patients in the control group, mild liver fibrosis group, and severe liver fibrosis group were 12.69 ± 1.78S, 12.53 ± 2.05S, and 12.04 ± 1.57S, respectively. The average blood flow (BF) values of patients in the control group, mild liver fibrosis group, and severe liver fibrosis group were 105.68 ± 15.57 mL 100 g-1·min-1, 116.07 ± 16.5 mL·100 g-1·min-1, and 110.39 ± 16.32 mL·100 g-1·min-1, respectively. Besides, the average blood volume (BV) values of patients in the control group, mild liver fibrosis group, and significant liver fibrosis group were 15.69 ± 4.35 mL·log-1, 16.97 ± 2.68 mL·log-1, and 16.11 ± 4.87 mL·100 g-1, respectively. According to statistics, the differences among the average HAF, MTT, BF, and BV values showed no statistical meaning. AI segmentation algorithm-based MSCT imaging could promote the diagnosis of liver cirrhosis and liver fibrosis effectively and offer new methods to clinical diagnosis of liver cirrhosis and liver fibrosis.

Deep learning with ultrasonography: automated classification of liver fibrosis using a deep convolutional neural network

OBJECTIVES

The aim of this study was to develop a deep convolutional neural network (DCNN) for the prediction of the METAVIR score using B-mode ultrasonography images.

METHODS

Datasets from two tertiary academic referral centers were used. A total of 13,608 ultrasonography images from 3446 patients who underwent surgical resection, biopsy, or transient elastography were used for training a DCNN for the prediction of the METAVIR score. Pathological specimens or estimated METAVIR scores derived from transient elastography were used as a reference standard. A four-class model (F0 vs. F1 vs. F23 vs. F4) was developed. Diagnostic performance of the algorithm was validated on a separate internal test set of 266 patients with 300 images and external test set of 572 patients with 1232 images. Performance in classification of cirrhosis was compared between the DCNN and five radiologists.

RESULTS

The accuracy of the four-class model was 83.5% and 76.4% on the internal and external test set, respectively. The area under the receiver operating characteristic curve (AUC) for classification of cirrhosis (F4) was 0.901 (95% confidence interval [CI], 0.865-0.937) on the internal test set and 0.857 (95% CI, 0.825-0.889) on the external test set, respectively. The AUC of the DCNN for classification of cirrhosis (0.857) was significantly higher than that of all five radiologists (AUC range, 0.656-0.816; p value < 0.05) using the external test set.

CONCLUSIONS

The DCNN showed high accuracy for determining METAVIR score using ultrasonography images and achieved better performance than that of radiologists in the diagnosis of cirrhosis.

KEY POINTS

• DCNN accurately classified the ultrasonography images according to the METAVIR score. • The AUROC of this algorithm for cirrhosis assessment was significantly higher than that of radiologists. • DCNN using US images may offer an alternative tool for monitoring liver fibrosis.

Factor analysis of the biochemical markers related to liver cirrhosis

Objectives:

The purpose of this study was to find the correlations between biochemical study and liver cirrhosis.

Methods:

The patients had liver biopsy to check the degree of their liver fibrosis, from August 2013 to August 2014 at the current medical center. In order to find the etiology of hepatitis, a research was done on gender, age, weight, and biochemical study through the investigation of subjects’ medical record and medical history. For biochemical study, we examined hemoglobin, platelets, albumin, aspartate aminotransferase (AST), alanine aminotransferase (ALT), total bilirubin, gamma-glutamyl transferase (GGT), prothrombin time (PT), and international normalised ratio (INR). We also analyzed the factors that are related to liver cirrhosis.

Results:

As a result, the patients at liver cirrhosis F≥2 stage showed 0.973, which is higher than the patients at FO stage with 0.943. F≥2 stage of hemoglobin was 0.544, which is lower than FO stage of hemoglobin with 0.817. Platelet count in F≥2 stage was 0.417, which is higher than FO stage with 0.074. For Albumin, F≥2 stage was 0.155 when F0 stage was 0.135. ATS’s F≥2 stage was 0.665, which is 6 times higher than FO stage with 0.100. Moreover, in the case of GGT, F≥2 stage was higher with 0.492 than FO stage with 0.078.

Conclusions:

In conclusion, it was confirmed that there is an increase in liver cirrhosis in the following general characteristics and biochemical factors: increase of age, increase of GGT, decrease of albumin, increase of the total bilirubin, and growth of INR (International Normalized Ratio).

Prediction of liver cirrhosis, using diagnostic imaging tools

Early diagnosis of liver cirrhosis is important. Ultrasound-guided liver biopsy is the gold standard for diagnosis of liver cirrhosis. However, its invasiveness and sampling bias limit the applicability of the method. Basic imaging for the diagnosis of liver cirrhosis has developed over the last few decades, enabling early detection of morphological changes of the liver by ultrasonography (US), computed tomography, and magnetic resonance imaging (MRI). They are also accurate diagnostic methods for advanced liver cirrhosis, for which early diagnosis is difficult. There are a number of ways to compensate for this difficulty, including texture analysis to more closely identify the homogeneity of hepatic parenchyma, elastography to measure the stiffness and elasticity of the liver, and perfusion studies to determine the blood flow volume, transit time, and velocity. Amongst these methods, elastography using US and MRI was found to be slightly easier, faster, and able to provide an accurate diagnosis. Early diagnosis of liver cirrhosis using MRI or US elastography is therefore a realistic alternative, but further research is still needed.

Low-energy electromagnetic fields promote proliferation of vascular smooth muscle cells

The rationale was to investigate the effects of low-energy electromagnetic fields (EMF) on the proliferation of bovine coronary and murine aortic smooth muscle cells (SMC). EMF were applied to SMC at field frequencies of 25, 50, or 100 Hz, and exposure time was set to 5, 15, or 30 minutes. Significant increases in SMC-counts compared with sham exposed controls were found for all EMF-frequencies tested. The effect was most pronounced for 50 Hz fields with maximum increases of 1.2-fold over controls. Sequential double exposure of mouse aortic SMC to 50 Hz fields revealed significantly enhanced cell proliferation by 1.2 fold compared with single exposure (p < 0.05). Experiments performed on bovine SMC also revealed significant increases in cell proliferation. The results demonstrate that EMF are capable of significantly enhancing the proliferation of vascular SMC. These results rise the question whether EMF would qualify as supportive means to angio-/arteriogenic approaches.