Prediction and Staging of Hepatic Fibrosis in Children with Hepatitis C Virus: A Machine Learning Approach

Investigation of the effectiveness of a classification method based on improved DAE feature extraction for hepatitis C prediction

Hepatitis C, a particularly dangerous form of viral hepatitis caused by hepatitis C virus (HCV) infection, is a major socio-economic and public health problem. Due to the rapid development of deep learning, it has become a common practice to apply deep learning to the healthcare industry to improve the effectiveness and accuracy of disease identification. In order to improve the effectiveness and accuracy of hepatitis C detection, this study proposes an improved denoising autoencoder (IDAE) and applies it to hepatitis C disease detection. Conventional denoising autoencoder introduces random noise at the input layer of the encoder. However, due to the presence of these features, encoders that directly add random noise may mask certain intrinsic properties of the data, making it challenging to learn deeper features. In this study, the problem of data information loss in traditional denoising autoencoding is addressed by incorporating the concept of residual neural networks into an enhanced denoising autoencoder. In our experimental study, we applied this enhanced denoising autoencoder to the open-source Hepatitis C dataset and the results showed significant results in feature extraction. While existing baseline machine learning methods have less than 90% accuracy and integrated algorithms and traditional autoencoders have only 95% correctness, the improved IDAE achieves 99% accuracy in the downstream hepatitis C classification task, which is a 9% improvement over a single algorithm, and a nearly 4% improvement over integrated algorithms and other autoencoders. The above results demonstrate that IDAE can effectively capture key disease features and improve the accuracy of disease prediction in hepatitis C data. This indicates that IDAE has the potential to be widely used in the detection and management of hepatitis C and similar diseases, especially in the development of early warning systems, progression prediction and personalised treatment strategies.

Thrombospondin-2 as a potential noninvasive biomarker of hepatocyte injury but not liver fibrosis in children with MAFLD: A preliminary study

Aim of the study

Metabolic-associated fatty liver disease (MAFLD) requires close monitoring due to its increased incidence and progression to fibrosis, cirrhosis and even hepatocellular carcinoma. The search for non-invasive markers to diagnose liver fibrosis is ongoing. The aim of our study was to evaluate the serum levels of growth differentiation factor-15 (GDF-15), thrombospondin-2 (TSP2), pentraxin 3 (PTX3) and angiopoietin-like protein 8 (ANGPTL8) in children with MAFLD.

Material and methods

Fifty-six overweight/obese children with suspected liver disease were included in this prospective study. MAFLD was diagnosed according to the latest consensus. Vibration-controlled transient elastography (TE) was performed to detect clinically significant liver fibrosis. Serum concentrations of GDF-15, TSP2, PTX3 and ANGPTL8 were measured by enzyme-linked immunosorbent assay (ELISA).

Results

Liver steatosis was diagnosed in abdominal ultrasound in 31 (55.36%) overweight/obese patients who were classified as the MAFLD group. Aspartate aminotransferase (AST)/platelet ratio (APRI) and liver stiffness measurement (LSM) values and TSP2 concentrations showed significantly higher values in patients in MAFLD than in the non-MAFLD group. TSP2 was significantly positively correlated with alanine transaminase (ALT), AST, γ-glutamyltransferase (GGT) and APRI in the study group. The receiver operating characteristics (ROC) analysis showed that the area under the curve (AUC) of LSM, APRI and serum TSP2 was significant for predicting MAFLD in obese children. In the multivariable regression model, LSM was the only significant parameter associated with the diagnosis of MAFLD in children.

Conclusions

TSP2 may be a potential biomarker of hepatocyte injury in pediatric patients with MAFLD. None of the examined biomarkers were found to be effective non-invasive markers of liver fibrosis in children.

Hybrid model for precise hepatitis-C classification using improved random forest and SVM method

Hepatitis C Virus (HCV) is a viral infection that causes liver inflammation. Annually, approximately 3.4 million cases of HCV are reported worldwide. A diagnosis of HCV in earlier stages helps to save lives. In the HCV review, the authors used a single ML-based prediction model in the current research, which encounters several issues, i.e., poor accuracy, data imbalance, and overfitting. This research proposed a Hybrid Predictive Model (HPM) based on an improved random forest and support vector machine to overcome existing research limitations. The proposed model improves a random forest method by adding a bootstrapping approach. The existing RF method is enhanced by adding a bootstrapping process, which helps eliminate the tree's minor features iteratively to build a strong forest. It improves the performance of the HPM model. The proposed HPM model utilizes a 'Ranker method' to rank the dataset features and applies an IRF with SVM, selecting higher-ranked feature elements to build the prediction model. This research uses the online HCV dataset from UCI to measure the proposed model's performance. The dataset is highly imbalanced; to deal with this issue, we utilized the synthetic minority over-sampling technique (SMOTE). This research performs two experiments. The first experiment is based on data splitting methods, K-fold cross-validation, and training: testing-based splitting. The proposed method achieved an accuracy of 95.89% for k = 5 and 96.29% for k = 10; for the training and testing-based split, the proposed method achieved 91.24% for 80:20 and 92.39% for 70:30, which is the best compared to the existing SVM, MARS, RF, DT, and BGLM methods. In experiment 2, the analysis is performed using feature selection (with SMOTE and without SMOTE). The proposed method achieves an accuracy of 41.541% without SMOTE and 96.82% with SMOTE-based feature selection, which is better than existing ML methods. The experimental results prove the importance of feature selection to achieve higher accuracy in HCV research.

Machine Learning Approaches for the Prediction of Hepatitis B and C Seropositivity

(1) Background: The identification of patients at risk for hepatitis B and C viral infection is a challenge for the clinicians and public health specialists. The aim of this study was to evaluate and compare the predictive performances of four machine learning-based models for the prediction of HBV and HCV status. (2) Methods: This prospective cohort screening study evaluated adults from the North-Eastern and South-Eastern regions of Romania between January 2022 and November 2022 who underwent viral hepatitis screening in their family physician's offices. The patients' clinical characteristics were extracted from a structured survey and were included in four machine learning-based models: support vector machine (SVM), random forest (RF), naïve Bayes (NB), and K nearest neighbors (KNN), and their predictive performance was assessed. (3) Results: All evaluated models performed better when used to predict HCV status. The highest predictive performance was achieved by KNN algorithm (accuracy: 98.1%), followed by SVM and RF with equal accuracies (97.6%) and NB (95.7%). The predictive performance of these models was modest for HBV status, with accuracies ranging from 78.2% to 97.6%. (4) Conclusions: The machine learning-based models could be useful tools for HCV infection prediction and for the risk stratification process of adult patients who undergo a viral hepatitis screening program.

Machine learning insights concerning inflammatory and liver-related risk comorbidities in non-communicable and viral diseases

The liver is a key organ involved in a wide range of functions, whose damage can lead to chronic liver disease (CLD). CLD accounts for more than two million deaths worldwide, becoming a social and economic burden for most countries. Among the different factors that can cause CLD, alcohol abuse, viruses, drug treatments, and unhealthy dietary patterns top the list. These conditions prompt and perpetuate an inflammatory environment and oxidative stress imbalance that favor the development of hepatic fibrogenesis. High stages of fibrosis can eventually lead to cirrhosis or hepatocellular carcinoma (HCC). Despite the advances achieved in this field, new approaches are needed for the prevention, diagnosis, treatment, and prognosis of CLD. In this context, the scientific com-munity is using machine learning (ML) algorithms to integrate and process vast amounts of data with unprecedented performance. ML techniques allow the integration of anthropometric, genetic, clinical, biochemical, dietary, lifestyle and omics data, giving new insights to tackle CLD and bringing personalized medicine a step closer. This review summarizes the investigations where ML techniques have been applied to study new approaches that could be used in inflammatory-related, hepatitis viruses-induced, and coronavirus disease 2019-induced liver damage and enlighten the factors involved in CLD development.

Development and multicenter validation of FIB-6: A novel, machine learning, simple bedside score to rule out liver cirrhosis and compensated advanced chronic liver disease in patients with chronic hepatitis C

BACKGROUND

Non-invasive tests (NITs), such as Fibrosis-4 index (FIB-4) and the aspartate aminotransferase-to-platelet ratio index (APRI), developed using classical statistical methods, are increasingly used for determining liver fibrosis stages and recommended in treatment guidelines replacing the liver biopsy. Application of conventional cutoffs of FIB-4 and APRI resulted in high rates of misclassification of fibrosis stages.

AIM

There is an unmet need for more accurate NITs that can overcome the limitations of FIB-4 and APRI.

PATIENTS AND METHODS

Machine learning with the random forest algorithm was used to develop a non-invasive index using retrospective data of 7238 patients with biopsy-proven chronic hepatitis C from two centers in Egypt; derivation dataset (n = 1821) and validation set in the second center (n = 5417). Receiver operator curve analysis was used to define cutoffs for different stages of fibrosis. Performance of the new score was externally validated in cohorts from two other sites in Egypt (n = 560) and seven different countries (n = 1317). Fibrosis stages were determined using the METAVIR score. Results were also compared with three established tools (FIB-4, APRI, and the aspartate aminotransferase-to-alanine aminotransferase ratio [AAR]).

RESULTS

Age in addition to readily available laboratory parameters such as aspartate, and alanine aminotransferases, alkaline phosphatase, albumin (g/dl), and platelet count (/cm³ ) correlated with the biopsy-derived stage of liver fibrosis in the derivation cohort and were used to construct the model for predicting the fibrosis stage by applying the random forest algorithm, resulting in an FIB-6 index, which can be calculated easily at http://fib6.elriah.info. Application of the cutoff values derived from the derivation group on the validation groups yielded very good performance in ruling out cirrhosis (negative predictive value [NPV] = 97.7%), compensated advance liver disease (NPV = 90.2%), and significant fibrosis (NPV = 65.7%). In the external validation groups from different countries, FIB-6 demonstrated higher sensitivity and NPV than FIB-4, APRI, and AAR.

CONCLUSION

FIB-6 score is a non-invasive, simple, and accurate test for ruling out liver cirrhosis and compensated advance liver disease in patients with chronic hepatitis C and performs better than APRI, FIB-4, and AAR.

Diagnosing the Stage of Hepatitis C Using Machine Learning

Hepatitis C is a prevalent disease in the world. Around 3 to 4 million new cases of Hepatitis C are reported every year across the globe. Effective, timely prediction of the disease can help people know about their Stage of Hepatitis C. To identify the Stage of disease, various noninvasive serum biochemical markers and clinical information of the patients have been used. Machine learning techniques have been an effective alternative tool for determining the Stage of this chronic disease of the liver to prevent biopsy side effects. In this study, an Intelligent Hepatitis C Stage Diagnosis System (IHSDS) empowered with machine learning is presented to predict the Stage of Hepatitis C in a human using Artificial Neural Network (ANN). The dataset obtained from the UCI machine learning repository contains 29 features, out of which the 19 most reverent are selected to conduct the study; 70% of the dataset is used for training and 30% for validation purposes. The precision value is compared with the proposed IHSDS with previously presented models. The proposed IHSDS has achieved 98.89% precision during training and 94.44% precision during validation.

Changes in Liver Stiffness and Noninvasive Fibrosis Scores in Egyptian Adolescents Successfully Treated with Ledipasvir-Sofosbuvir for Chronic Hepatitis C Virus Infection

OBJECTIVE

To assess changes in noninvasive liver fibrosis measurements after chronic hepatitis C eradication by direct-acting antivirals in Egyptian adolescents.

STUDY DESIGN

Liver stiffness measurement (LSM), by vibration-controlled transient elastography and noninvasive fibrosis scores (Firbosis-4, aspartate aminotransferase-platelet ratio index), was obtained before and 12 months after eradication with ledipasvir-sofosbuvir. The primary outcome was a more than 30% decrease in LSM with resulting fibrosis stage regression for initial fibrosis of F2 or higher and nonprogression of F0-F1, using the Ishak score (F0-F6). The secondary outcome was change in noninvasive fibrosis scores after treatment.

RESULTS

Analyzing 85 patients, the median baseline LSM was 5.8 (IQR, 4.2-6.5) and at follow-up 5.1 kPa (IQR, 4-6 kPa) (P = .045); 62 (73%) met the primary outcome, 16 patients (19%) experienced regression, and 46 (54%) nonprogression of LSM. Of 18 with initial fibrosis of F2 0r higher, 13 regressed to F0-F1 and 2 from F6 to F5, 1 unchanged at F3, and 1 increased to F3 and 1 to F4. Among 67 patients with a baseline fibrosis of F0-F1, 62 were unchanged and 5 increased-4 to F2 and 1 to F3. Although 23 (27%) had a more than 30% LSM increase, only 7 (8%), with associated comorbidities (4 β-thalassemia, 3 hepatic steatosis), had increased fibrosis stage. The median baseline FIB-4 and aspartate aminotransferase-platelet ratio index scores were 0.34 (IQR, 0.22-0.47) and 0.35 (0.24-0.57), and at follow-up 0.3 (IQR, 0.22-0.34) and 0.2 (0.18-2.8) (P < .001, <.001), respectively.

CONCLUSIONS

Chronic hepatitis C eradication by direct-acting antiviral agents in Egyptian adolescents was associated with nonprogression or regression of liver fibrosis, by noninvasive fibrosis measurements, at 12 months after treatment in the majority of cases.