Laboratory parameter-based machine learning model for excluding non-alcoholic fatty liver disease (NAFLD) in the general population

Establishment of a machine learning predictive model for non-alcoholic fatty liver disease: A longitudinal cohort study

BACKGROUND AND AIMS

Non-alcoholic fatty liver disease (NAFLD) is a common chronic liver disease, which lacks effective drug treatments. This study aimed to construct an eXtreme Gradient Boosting (XGBoost) prediction model to identify or evaluate potential NAFLD patients.

METHODS AND RESULTS

We conducted a longitudinal study of 22,140 individuals from the Beijing Health Management Cohort. Variable filtering was performed using the least absolute shrinkage and selection operator. Random Over Sampling Examples was used to address imbalanced data. Next, the XGBoost model and the other three machine learning (ML) models were built using balanced data. Finally, the variable importance of the XGBoost model was ranked. Among four ML algorithms, we got that the XGBoost model outperformed the other models with the following results: accuracy of 0.835, sensitivity of 0.835, specificity of 0.834, Youden index of 0.669, precision of 0.831, recall of 0.835, F-1 score of 0.833, and an area under the curve of 0.914. The top five variables with the greatest impact on the onset of NAFLD were aspartate aminotransferase, cardiometabolic index, body mass index, alanine aminotransferase, and triglyceride-glucose index.

CONCLUSION

The predictive model based on the XGBoost algorithm enables early prediction of the onset of NAFLD. Additionally, assessing variable importance provides valuable insights into the prevention and treatment of NAFLD.

Diagnosis and non-invasive assessment of MASLD in type 2 diabetes and obesity

BACKGROUND

Metabolic dysfunction-associated steatotic liver disease (MASLD) is currently the most common chronic liver disease and an important cause of cirrhosis and hepatocellular carcinoma. It is strongly associated with type 2 diabetes and obesity. Because of the huge number of patients at risk of MASLD, it is imperative to use non-invasive tests appropriately.

AIMS

To provide a narrative review on the performance and limitations of non-invasive tests, with a special emphasis on the impact of diabetes and obesity.

METHODS

We searched PubMed and Cochrane databases for articles published from 1990 to August 2023.

RESULTS

Abdominal ultrasonography remains the primary method to diagnose hepatic steatosis, while magnetic resonance imaging proton density fat fraction is currently the gold standard to quantify steatosis. Simple fibrosis scores such as the Fibrosis-4 index are well suited as initial assessment in primary care and non-hepatology settings to rule out advanced fibrosis and future risk of liver-related complications. However, because of its low positive predictive value, an abnormal test should be followed by specific blood (e.g. Enhanced Liver Fibrosis score) or imaging biomarkers (e.g. vibration-controlled transient elastography and magnetic resonance elastography) of fibrosis. Some non-invasive tests of fibrosis appear to be less accurate in patients with diabetes. Obesity also affects the performance of abdominal ultrasonography and transient elastography, whereas magnetic resonance imaging may not be feasible in some patients with severe obesity.

CONCLUSIONS

This article highlights issues surrounding the clinical application of non-invasive tests for MASLD in patients with type 2 diabetes and obesity.

Diagnosis and assessment of disease severity in patients with nonalcoholic fatty liver disease

Nonalcoholic fatty liver disease (NAFLD) includes simple steatosis, nonalcoholic steatohepatitis (NASH), fibrosis, and eventually cirrhosis and hepatocellular carcinoma (HCC). The diagnosis of NAFLD is based on the detection of excess fat disposition in the liver, which is the first step to trigger further evaluation of NAFLD, including necroinflammation and fibrosis. In this review, we discuss non-invasive biomarkers and imaging tools that are currently and potentially available for different features (steatosis, necroinflammation and fibrosis) and disease severity assessment of NAFLD. In the past 2 decades, advances in non-invasive tests of fibrosis have transformed the management of NAFLD. Blood and imaging biomarkers have already been evaluated in multiple studies for the diagnosis of fibrosis and cirrhosis. Among the various histological features of NAFLD, the degree of fibrosis has the strongest correlation with liver-related morbidity and mortality. Non-invasive tests of fibrosis have been shown to predict liver-related outcomes, both in the general population and among patients with NAFLD. What is lacking, however, is good data to support the use of non-invasive tests as monitoring and response biomarkers. With the conclusion of several large phase 3 studies in the next few years, the availability of paired liver biopsy, non-invasive test and clinical outcome data will likely advance the field and shed light on new biomarkers and the way to use various non-invasive tests in a longitudinal manner.

Artificial Intelligence in Liver Diseases: Recent Advances

Liver diseases cause a significant burden on public health worldwide. In spite of great advances during recent years, there are still many challenges in the diagnosis and treatment of liver diseases. During recent years, artificial intelligence (AI) has been widely used for the diagnosis, risk stratification, and prognostic prediction of various diseases based on clinical datasets and medical images. Accumulative studies have shown its performance for diagnosing patients with nonalcoholic fatty liver disease and liver fibrosis and assessing their severity, and for predicting treatment response and recurrence of hepatocellular carcinoma, outcomes of liver transplantation recipients, and risk of drug-induced liver injury. Herein, we aim to comprehensively summarize the current evidence regarding diagnostic, prognostic, and/or therapeutic role of AI in these common liver diseases.

A data-driven approach to decode metabolic dysfunction-associated steatotic liver disease

Metabolic dysfunction-associated steatotic liver disease (MASLD), defined by the presence of liver steatosis together with at least one out of five cardiometabolic factors, is the most common cause of chronic liver disease worldwide, affecting around one in three people. Yet the clinical presentation of MASLD and the risk of progression to cirrhosis and adverse clinical outcomes is highly variable. It, therefore, represents both a global public health threat and a precision medicine challenge. Artificial intelligence (AI) is being investigated in MASLD to develop reproducible, quantitative, and automated methods to enhance patient stratification and to discover new biomarkers and therapeutic targets in MASLD. This review details the different applications of AI and machine learning algorithms in MASLD, particularly in analyzing electronic health record, digital pathology, and imaging data. Additionally, it also describes how specific MASLD consortia are leveraging multimodal data sources to spark research breakthroughs in the field. Using a new national-level 'data commons' (SteatoSITE) as an exemplar, the opportunities, as well as the technical challenges of large-scale databases in MASLD research, are highlighted.

Machine learning approaches to enhance diagnosis and staging of patients with MASLD using routinely available clinical information

AIMS

Metabolic dysfunction Associated Steatotic Liver Disease (MASLD) outcomes such as MASH (metabolic dysfunction associated steatohepatitis), fibrosis and cirrhosis are ordinarily determined by resource-intensive and invasive biopsies. We aim to show that routine clinical tests offer sufficient information to predict these endpoints.

METHODS

Using the LITMUS Metacohort derived from the European NAFLD Registry, the largest MASLD dataset in Europe, we create three combinations of features which vary in degree of procurement including a 19-variable feature set that are attained through a routine clinical appointment or blood test. This data was used to train predictive models using supervised machine learning (ML) algorithm XGBoost, alongside missing imputation technique MICE and class balancing algorithm SMOTE. Shapley Additive exPlanations (SHAP) were added to determine relative importance for each clinical variable.

RESULTS

Analysing nine biopsy-derived MASLD outcomes of cohort size ranging between 5385 and 6673 subjects, we were able to predict individuals at training set AUCs ranging from 0.719-0.994, including classifying individuals who are At-Risk MASH at an AUC = 0.899. Using two further feature combinations of 26-variables and 35-variables, which included composite scores known to be good indicators for MASLD endpoints and advanced specialist tests, we found predictive performance did not sufficiently improve. We are also able to present local and global explanations for each ML model, offering clinicians interpretability without the expense of worsening predictive performance.

CONCLUSIONS

This study developed a series of ML models of accuracy ranging from 71.9-99.4% using only easily extractable and readily available information in predicting MASLD outcomes which are usually determined through highly invasive means.

Algorithms for Early Detection of Silent Liver Fibrosis in the Primary Care Setting

More than one-third of the adult world population has steatotic liver disease (SLD), with a few percent of individuals developing cirrhosis after decades of silent liver fibrosis accumulation. Lack of systematic early detection causes most patients to be diagnosed late, after decompensation, when treatment has limited effect and survival is poor. Unfortunately, no isolated screening test in primary care can sufficiently predict advanced fibrosis from SLD. Recent efforts, therefore, combine several parameters into screening algorithms, to increase diagnostic accuracy. Besides patient selection, for example, by specific characteristics, algorithms include nonpatented or patented blood tests and liver stiffness measurements using elastography-based techniques. Algorithms can be composed as a set of sequential tests, as recommended by most guidelines on primary care pathways. Future use of algorithms that are easy to interpret, cheap, and semiautomatic will improve the management of patients with SLD, to the benefit of global health care systems.

Clinical Predictive Score for Identifying Metabolic Dysfunction-Associated Steatotic Liver Disease in Individuals with Prediabetes Using Transient Elastography

Scoring systems for metabolic dysfunction-associated steatotic liver disease (MASLD) in individuals with prediabetes have not been extensively explored. This study aimed to investigate the prevalence of MASLD and to develop predictive tools for its detection in high cardiometabolic people with prediabetes. A cross-sectional study was conducted using baseline data from the prediabetes cohort. All participants underwent transient elastography to assess liver stiffness. MASLD was defined using a controlled attenuation parameter value > 275 dB/m and/or a liver stiffness measurement ≥ 7.0 kPa. Cases with secondary causes of hepatic steatosis were excluded. Out of 400 participants, 375 were included. The observed prevalence of MASLD in individuals with prediabetes was 35.7%. The most effective predictive model included FPG ≥ 110 mg/dL; HbA1c ≥ 6.0%; sex-specific cutoffs for HDL; ALT ≥ 30 IU/L; and BMI levels. This model demonstrated good predictive performance with an AUC of 0.80 (95% CI 0.73-0.86). At a cutoff value of 4.5, the sensitivity was 70.7%, the specificity was 72.3%, the PPV was 58.8%, and the NPV was 81.5%. Our predictive model is practical, easy to use, and relies on common parameters. The scoring system should aid clinicians in determining when further investigations of MASLD are warranted among individuals with prediabetes, especially in settings with limited resources.

Duration of type 2 diabetes and liver-related events in nonalcoholic fatty liver disease: A landmark analysis

BACKGROUND AND AIMS

We aimed to determine the impact of the duration of type 2 diabetes (T2D) on the risk of liver-related events and all-cause mortality in patients with NAFLD.

APPROACH AND RESULTS

We conducted a territory-wide cohort study of adult patients with NAFLD diagnosed between January 1, 2000, and July 31, 2021, in Hong Kong. T2D was defined by the use of any antidiabetic agents, laboratory tests, and/or diagnosis codes. The primary endpoint was liver-related events, defined as a composite endpoint of HCC and cirrhotic complications. To conduct a more granular assessment of the duration of T2D, we employed landmark analysis in four different ages of interest (biological age of 40, 50, 60, and 70 years). By multivariable analysis with adjustment of non-liver-related deaths, compared with patients without diabetes at age 60 (incidence rate of liver-related events: 0.70 per 1,000 person-years), the adjusted subdistribution HR (SHR) of liver-related events was 2.51 (95% CI: 1.32-4.77; incidence rate: 2.26 per 1,000 person-years) in patients with T2D duration < 5 years, 3.16 (95% CI: 1.59-6.31; incidence rate: 2.54 per 1,000 person-years) in those with T2D duration of 6-10 years, and 6.20 (95% CI: 2.62-14.65; incidence rate: 4.17 per 1000 person-years) in those with T2D duration more than 10 years. A similar association between the duration of T2D and all-cause mortality was also observed.

CONCLUSIONS

Longer duration of T2D is significantly associated with a higher risk of liver-related events and all-cause mortality in patients with NAFLD.

A Cross-sectional Real-life Study of the Prevalence, Severity, and Determinants of Metabolic Dysfunction-associated Fatty Liver Disease in Type 2 Diabetes Patients

Background and Aims

Most data on liver assessment in type 2 diabetes mellitus (T2DM) patients are from retrospective cohorts with selection bias. We aimed at appraising the feasibility, results, and benefits of an outpatient systematic noninvasive screening for metabolic dysfunction-associated fatty liver disease (MAFLD) severity and determinants in T2DM patients.

Methods

We conducted a 50-week cross-sectional study enrolling adult T2DM outpatients from a diabetes clinic. An algorithm based on guidelines was applied using simple bioclinical scores and, if applicable, ultrasound and/or elastometry.

Results

Two hundred and thirteen patients were included. Mean age and body mass index were 62 years and 31 kg/m2 and 29% of patients had abnormal transaminase levels. The acceptance rate of additional liver examinations was 92%. The prevalence of MAFLD, advanced fibrosis and cirrhosis was 87%, 11%, and 4%, respectively. More than half of the cases of advanced fibrosis had not been suspected and were detected by this screening. MAFLD was associated with poor glycemic control, elevated transaminases, low HDL-C and the absence of peripheral arterial disease. Advanced fibrosis was linked to high waist circumference and excessive alcohol consumption, which should be interpreted with caution owing to the small number of patients reporting excessive consumption.

Conclusions

Simple bioclinical tools allowed routine triage of T2DM patients for MAFLD severity, with high adherence of high-risk patients to subsequent noninvasive exams.

Liver Fat Scores for Noninvasive Diagnosis and Monitoring of Nonalcoholic Fatty Liver Disease in Epidemiological and Clinical Studies

Nonalcoholic fatty liver disease (NAFLD) is strongly associated with the metabolic syndrome and type 2 diabetes and independently contributes to long-term complications. Being often asymptomatic but reversible, it would require population-wide screening, but direct diagnostics are either too invasive (liver biopsy), costly (MRI) or depending on the examiner's expertise (ultrasonography). Hepatosteatosis is usually accommodated by features of the metabolic syndrome (e.g. obesity, disturbances in triglyceride and glucose metabolism), and signs of hepatocellular damage, all of which are reflected by biomarkers, which poorly predict NAFLD as single item, but provide a cheap diagnostic alternative when integrated into composite liver fat indices. Fatty liver index, NAFLD LFS, and hepatic steatosis index are common and accurate indices for NAFLD prediction, but show limited accuracy for liver fat quantification. Other indices are rarely used. Hepatic fibrosis scores are commonly used in clinical practice, but their mandatory reflection of fibrotic reorganization, hepatic injury or systemic sequelae reduces sensitivity for the diagnosis of simple steatosis. Diet-induced liver fat changes are poorly reflected by liver fat indices, depending on the intervention and its specific impact of weight loss on NAFLD. This limited validity in longitudinal settings stimulates research for new equations. Adipokines, hepatokines, markers of cellular integrity, genetic variants but also simple and inexpensive routine parameters might be potential components. Currently, liver fat indices lack precision for NAFLD prediction or monitoring in individual patients, but in large cohorts they may substitute nonexistent imaging data and serve as a compound biomarker of metabolic syndrome and its cardiometabolic sequelae.

Hepatic steatosis in women with polycystic ovary syndrome

BACKGROUND

This multi-center, cross-sectional study intended to explore the prevalence and risk factors of nonalcoholic fatty liver disease (NAFLD) and metabolic dysfunction-associated fatty liver disease (MAFLD) in patients with polycystic ovary syndrome (PCOS).

METHODS

Patients who met the PCOS Rotterdam diagnostic criteria were enrolled in 6 centers in China, and age-matched healthy volunteers were also recruited. Data were collected including medical history, physical characteristics, and blood tests (liver function, blood lipids, blood glucose and insulin, sex hormones, etc.). Transvaginal or transrectal ultrasound was employed to identify polycystic ovarian morphology (PCOM). The serological score Liver Fat Score (LFS) >-0.640 was used for the diagnosis of NAFLD, and the diagnosis of MAFLD was made according to the 2020 new definition.

RESULTS

A total of 217 PCOS patients and 72 healthy controls were included. PCOS patients had impaired glucose and lipid metabolism, higher liver enzymes and LFS. Both NAFLD (33.6%) and MAFLD (42.8%) was more prevalent in PCOS patients than in controls (4.2%, P < 0.001). Logistic regression results showed that HOMA-IR ≥ 3.54 and ALT ≥ 18.2 were independently associated with NAFLD (P < 0.001) and MAFLD (P ≤ 0.001). The prevalence of NAFLD was significantly higher in PCOS patients with free androgen index (FAI) > 8 (53.8% versus 17.4%, P < 0.001) and BMI ≥ 24 kg/m2 (57.3%, 11.3%, P < 0.001).

CONCLUSION

The prevalence of NAFLD/MAFLD in PCOS patients was significantly higher than that in healthy controls and was independently associated with HOMA-IR and ALT. PCOS patients with overweight and elevated FAI have a higher prevalence of fatty liver.

Nonalcoholic fatty liver disease (NAFLD) detection and deep learning in a Chinese community-based population

OBJECTIVES

We aimed to develop and validate a deep learning system (DLS) by using an auxiliary section that extracts and outputs specific ultrasound diagnostic features to improve the explainable, clinical relevant utility of using DLS for detecting NAFLD.

METHODS

In a community-based study of 4144 participants with abdominal ultrasound scan in Hangzhou, China, we sampled 928 (617 [66.5%] females, mean age: 56 years ± 13 [standard deviation]) participants (2 images per participant) to develop and validate DLS, a two-section neural network (2S-NNet). Radiologists' consensus diagnosis classified hepatic steatosis as none steatosis, mild, moderate, and severe. We also explored the NAFLD detection performance of six one-section neural network models and five fatty liver indices on our data set. We further evaluated the influence of participants' characteristics on the correctness of 2S-NNet by logistic regression.

RESULTS

Area under the curve (AUROC) of 2S-NNet for hepatic steatosis was 0.90 for ≥ mild, 0.85 for ≥ moderate, and 0.93 for severe steatosis, and was 0.90 for NAFLD presence, 0.84 for moderate to severe NAFLD, and 0.93 for severe NAFLD. The AUROC of NAFLD severity was 0.88 for 2S-NNet, and 0.79-0.86 for one-section models. The AUROC of NAFLD presence was 0.90 for 2S-NNet, and 0.54-0.82 for fatty liver indices. Age, sex, body mass index, diabetes, fibrosis-4 index, android fat ratio, and skeletal muscle via dual-energy X-ray absorptiometry had no significant impact on the correctness of 2S-NNet (p > 0.05).

CONCLUSIONS

By using two-section design, 2S-NNet had improved the performance for detecting NAFLD with more explainable, clinical relevant utility than using one-section design.

KEY POINTS

• Based on the consensus review derived from radiologists, our DLS (2S-NNet) had an AUROC of 0.88 by using two-section design and yielded better performance for detecting NAFLD than using one-section design with more explainable, clinical relevant utility. • The 2S-NNet outperformed five fatty liver indices with the highest AUROCs (0.84-0.93 vs. 0.54-0.82) for different NAFLD severity screening, indicating screening utility of deep learning-based radiology may perform better than blood biomarker panels in epidemiology. • The correctness of 2S-NNet was not significantly influenced by individual's characteristics, including age, sex, body mass index, diabetes, fibrosis-4 index, android fat ratio, and skeletal muscle via dual-energy X-ray absorptiometry.

Machine learning models predict liver steatosis but not liver fibrosis in a prospective cohort study

INTRODUCTION

Screening for liver fibrosis continues to rely on laboratory panels and non-invasive tests such as FIB-4-score and transient elastography. In this study, we evaluated the potential of machine learning (ML) methods to predict liver steatosis on abdominal ultrasound and liver fibrosis, namely the intermediate-high risk of advanced fibrosis, in individuals participating in a screening program for colorectal cancer.

METHODS

We performed ultrasound on 5834 patients admitted between 2006 and 2020, and transient elastography on a subset of 1240 patients. Steatosis on ultrasound was diagnosed if liver areas showed a significantly increased echogenicity compared to the renal parenchyma. Liver fibrosis was defined as a liver stiffness measurement ≥8 kPa in transient elastography. We evaluated the performance of three algorithms, namely Extreme Gradient Boosting, Feed-Forward neural network and Logistic Regression, deriving the models using data from patients admitted from January 2007 up to January 2016 and prospectively evaluating on the data of patients admitted from January 2016 up to March 2020. We also performed a performance comparison with the standard clinical test based on Fibrosis-4 Index (FIB-4).

RESULTS

The mean age was 58±9 years with 3036 males (52%). Modelling laboratory parameters, clinical parameters, and data on eight food types/dietary patterns, we achieved high performance in predicting liver steatosis on ultrasound with AUC of 0.87 (95% CI [0.87-0.87]), and moderate performance in predicting liver fibrosis with AUC of 0.75 (95% CI [0.74-0.75]) using XGBoost machine learning algorithm. Patient-reported variables did not significantly improve predictive performance. Gender-specific analyses showed significantly higher performance in males with AUC of 0.74 (95% CI [0.73-0.74]) in comparison to female patients with AUC of 0.66 (95% CI [0.65-0.66]) in prediction of liver fibrosis. This difference was significantly smaller in prediction of steatosis with AUC of 0.85 (95% CI [0.83-0.87]) in female patients, in comparison to male patients with AUC of 0.82 (95% CI [0.80-0.84]).

CONCLUSION

ML based on point-prevalence laboratory and clinical information predicts liver steatosis with high accuracy and liver fibrosis with moderate accuracy. The observed gender differences suggest the need to develop gender-specific models.

Perioperative predictors of outcome of hepatectomy for HBV-related hepatocellular carcinoma

Hepatitis B virus (HBV) is identified as a major risk factor for hepatocellular carcinoma (HCC), resulting in so-called hepatitis B virus-related hepatocellular carcinoma (HBV-related HCC). Hepatectomy for HCC is acknowledged as an efficient treatment strategy, especially for early HCC. Furthermore, patients with advanced HCC can still obtain survival benefits through surgical treatment combined with neoadjuvant therapy, adjuvant therapy, transcatheter arterial chemoembolization, and radiofrequency ablation. Therefore, preoperative and postoperative predictors of HBV-related HCC have crucial indicative functions for the follow-up treatment of patients with feasible hepatectomy. This review covers a variety of research results on preoperative and postoperative predictors of hepatectomy for HBV-related HCC over the past decade and in previous landmark studies. The relevant contents of Hepatitis C virus-related HCC, non-HBV non-HCV HCC, and the artificial intelligence application in this field are briefly addressed in the extended content. Through the integration of this review, a large number of preoperative and postoperative factors can predict the prognosis of HBV-related HCC, while most of the predictors have no standardized thresholds. According to the characteristics, detection methods, and application of predictors, the predictors can be divided into the following categories: 1. serological and hematological predictors, 2. genetic, pathological predictors, 3. imaging predictors, 4. other predictors, 5. analysis models and indexes. Similar results appear in HCV-related HCC, non-HBV non-HCV HCC. Predictions based on AI and big biological data are actively being applied. A reasonable prediction model should be established based on the economic, health, and other levels in specific countries and regions.

Obesity, non-alcoholic fatty liver disease and hepatocellular carcinoma: current status and therapeutic targets

Obesity is a global epidemic and overwhelming evidence indicates that it is a risk factor for numerous cancers, including hepatocellular carcinoma (HCC), the third leading cause of cancer-related deaths worldwide. Obesity-associated hepatic tumorigenesis develops from nonalcoholic fatty liver disease (NAFLD), progressing to nonalcoholic steatohepatitis (NASH), cirrhosis and ultimately to HCC. The rising incidence of obesity is resulting in an increased prevalence of NAFLD and NASH, and subsequently HCC. Obesity represents an increasingly important underlying etiology of HCC, in particular as the other leading causes of HCC such as hepatitis infection, are declining due to effective treatments and vaccines. In this review, we provide a comprehensive overview of the molecular mechanisms and cellular signaling pathways involved in the pathogenesis of obesity-associated HCC. We summarize the preclinical experimental animal models available to study the features of NAFLD/NASH/HCC, and the non-invasive methods to diagnose NAFLD, NASH and early-stage HCC. Finally, since HCC is an aggressive tumor with a 5-year survival of less than 20%, we will also discuss novel therapeutic targets for obesity-associated HCC and ongoing clinical trials.

The role of artificial intelligence in hepatology research and practice

PURPOSE OF REVIEW

The use of artificial intelligence (AI) in examining large data sets has recently gained considerable attention to evaluate disease epidemiology, management approaches, and disease outcomes. The purpose of this review is to summarize the current role of AI in contemporary hepatology practice.

RECENT FINDINGS

AI was found to be diagnostically valuable in the evaluation of liver fibrosis, detection of cirrhosis, differentiation between compensated and decompensated cirrhosis, evaluation of portal hypertension, detection and differentiation of particular liver masses, preoperative evaluation of hepatocellular carcinoma as well as response to treatment and estimation of graft survival in patients undergoing liver transplantation. AI additionally holds great promise in examination of structured electronic health records data as well as in examination of clinical text (using various natural language processing approaches). Despite its contributions, AI has several limitations, including the quality of existing data, small cohorts with possible sampling bias and the lack of well validated easily reproducible models.

SUMMARY

AI and deep learning models have extensive applicability in assessing liver disease. However, multicenter randomized controlled trials are indispensable to validate their utility.

Clinical Characteristics and Outcomes of Nonalcoholic Fatty Liver Disease-Associated Hepatocellular Carcinoma in the United States

BACKGROUND & AIMS

The extent to which nonalcoholic fatty liver disease (NAFLD) contributes to hepatocellular carcinoma (HCC) prevalence in contemporary practices and whether there are any etiologic differences in surveillance receipt, tumor stage, and overall survival (OS) remain unclear. We aimed to estimate the burden of NAFLD-related HCC and magnitude of associations with surveillance receipt, clinical presentation, and outcomes in a contemporary HCC cohort.

METHODS

In a cohort of HCC patients from the Surveillance, Epidemiology and End Results-Medicare database between 2011 and 2015, we used multivariable logistic regression to identify factors associated with surveillance receipt, early-stage tumor detection, and curative treatment. Cox regression was used to identify factors associated with OS.

RESULTS

Among 5098 HCC patients, NAFLD was the leading etiology, accounting for 1813 cases (35.6%). Compared with those with hepatitis C-related HCC, NAFLD was associated with lower HCC surveillance receipt (adjusted odds ratio, 0.22; 95% confidence interval [CI], 0.17-0.28), lower early-stage HCC detection (adjusted odds ratio, 0.49; 95% CI, 0.40-0.60), and modestly worse OS (adjusted hazard ratio, 1.20; 95% CI, 1.09-1.32). NAFLD subgroup analysis showed that early-stage HCC, absence of ascites/hepatic encephalopathy, surveillance, and curative treatment receipt were associated with improved OS. NAFLD patients with coexisting liver disease were more likely to have surveillance, early-stage detection, curative treatment, and improved OS than NAFLD patients without coexisting liver diseases.

CONCLUSIONS

NAFLD is the leading etiology of HCC among Medicare beneficiaries. Compared with other etiologies, NAFLD was associated with lower HCC surveillance receipt, early-stage detection, and modestly poorer survival. Multifaceted interventions for improving surveillance uptake are needed to improve prognosis of patients with NAFLD-related HCC.

Association between the dual use of electronic and conventional cigarettes and NAFLD status in Korean men

INTRODUCTION

This study investigated the association between smoking types, including dual use (usage of both combustible cigarettes and e-cigarettes), and non-alcoholic fatty liver disease (NAFLD) status in Korean men.

METHODS

Data from the 7th and 8th Korea National Health and Nutrition Examination Survey (KNHANES) 2016-2020 were used. The presence of NAFLD was defined by the respective cut-off values for the Hepatic Steatosis Index (HSI), NAFLD Ridge Score (NRS), and Korea National Health and Nutrition Examination Survey NAFLD score (KNS). Multivariate logistic regression analyses were used to determine the associations between smoking types and NAFLD as determined by HSI, NRS, and KNS.

RESULTS

After adjustment for confounders, an independent association was observed between dual use and NAFLD (HSI: AOR=1.47; 95% CI: 1.08-1.99, p=0.014; NRS: AOR=2.21; 95% CI: 1.70-2.86, p=0.000; KNS: AOR=1.35; 95% CI: 1.01-1.81, p=0.045). Cigarette only smokers also had significantly higher odds of NAFLD compared to never smokers for all of the NAFLD indices (HSI: AOR=1.22; 95% CI: 1.05-1.42, p=0.008; NRS: AOR=2.13; 95% CI: 1.87-2.42, p=0.000; KNS: AOR=1.33; 95% CI: 1.14-1.55, p=0.000). In subgroup analyses, no significant interaction effects were found for age, BMI, alcohol consumption, income, physical activity, and the diagnosis of T2DM. Moreover, cigarette only smokers and dual users differed significantly in terms of log-transformed urine cotinine and pack-years. The relationship between smoking type and pack-years was attenuated after stratification by age.

CONCLUSIONS

This study shows that the dual use of e-cigarettes and combustible cigarettes is associated with NAFLD. Age differences may explain why dual users, with a greater proportion of young people, appear to have fewer pack-years than cigarette only smokers. Further research should be conducted to investigate the adverse effects of dual use on hepatic steatosis.

The development of a machine learning algorithm for early detection of viral hepatitis B infection in Nigerian patients

Access to Hepatitis B Virus (HBV) testing for people in low-resource settings has long been challenging due to the gold standard, enzyme immunoassay, being prohibitively expensive, and requiring specialised skills and facilities that are not readily available, particularly in remote and isolated laboratories. Routine pathology data in tandem with cutting-edge machine learning shows promising diagnostic potential. In this study, recursive partitioning ("trees") and Support Vector Machines (SVMs) were applied to interrogate patient dataset (n = 916) that comprised results for Hepatitis B Surface Antigen (HBsAg) and routine clinical chemistry and haematology blood tests. These algorithms were used to develop a predictive diagnostic model of HBV infection. Our SVM-based diagnostic model of infection (accuracy = 85.4%, sensitivity = 91%, specificity = 72.6%, precision = 88.2%, F1-score = 0.89, Area Under the Receiver Operating Curve, AUC = 0.90) proved to be highly accurate for discriminating HBsAg positive from negative patients, and thus rivals with immunoassay. Therefore, we propose a predictive model based on routine blood tests as a novel diagnostic for early detection of HBV infection. Early prediction of HBV infection via routine pathology markers and pattern recognition algorithms will offer decision-support to clinicians and enhance early diagnosis, which is critical for optimal clinical management and improved patient outcomes.

Trends in risk factor control and treatment among patients with non-alcoholic fatty liver disease and type 2 diabetes between 2000 and 2020: A territory-wide study

BACKGROUND & AIMS

We aimed to determine the trends in risk factor control and treatment among patients with non-alcoholic fatty liver disease (NAFLD) and type 2 diabetes (T2D) in 2000-2020.

METHODS

We conducted a territory-wide cohort study of adult patients with NAFLD and T2D diagnosed between 1 January 2000 and 31 July 2021 in Hong Kong. T2D was defined by use of any anti-diabetic agents, laboratory tests and/or diagnosis codes.

RESULTS

This study included 16,084 patients with NAFLD and T2D (mean age, 54.8 ± 12.0 years; 7124 male [44.3%]). The percentage of patients achieving individualised haemoglobin A_1c (HbA_1c ) targets increased from 44.5% (95% confidence interval [CI], 42.9-46.1) to 64.8% (95% CI, 64.1-65.5), and percentage of patients achieving individualised low-density lipoprotein-cholesterol (LDL-C) targets increased from 23.3% (95% CI, 21.9-24.7) to 54.3% (95% CI, 53.5-55.1) from 2000-2005 to 2016-2020, whereas percentage of patients achieving blood pressure control (<140/90 mm Hg) remained static at 53.1-57.2%. Combination therapy for diabetes increased, especially among those with poor glycaemic control, but there was no increase in combination therapy for hypertension. Fewer cirrhotic patients achieved blood pressure control and individualised LDL-C targets, but they were more likely to achieve individualised HbA_1c targets than non-cirrhotics. Metformin and statins were underused in cirrhotic patients. Younger patients (18-44 years) were less likely to achieve individualised HbA_1c targets than middle-aged (45-64 years) and older ones (≥65 years).

CONCLUSIONS

From 2000 to 2020, glycaemic and lipid control improved significantly, whereas blood pressure control remained static among patients with NAFLD and T2D.

Noninvasive serum biomarkers for liver steatosis in nonalcoholic fatty liver disease: Current and future developments

Nonalcoholic fatty liver disease (NAFLD) affects approximately 30% of the population worldwide and includes nonalcoholic fatty liver, nonalcoholic steatohepatitis (NASH), and cirrhosis. Since NAFLD-associated diseases begin with steatosis, the early diagnosis of steatosis helps to prevent the progression of NASH and fibrosis. In addition, more convenient and easily diagnosable serum biomarkers are becoming crucial in disease diagnosis. In this report, we summarize the known serum biomarkers for liver steatosis and provide guidance for their application in clinical practice.

Artificial intelligence models for the diagnosis and management of liver diseases

With the development of more advanced methods for the diagnosis and treatment of diseases, the data required for medical care are becoming complex, and misinterpretation of information due to human error may result in serious consequences. Human error can be avoided with the support of artificial intelligence (AI). AI models trained with various medical data for diagnosis and management of liver diseases have been applied to hepatitis, fatty liver disease, liver cirrhosis, and liver cancer. Some of these models have been reported to outperform human experts in terms of performance, indicating their potential for supporting clinical practice given their high-speed output. This paper summarizes the recent advances in AI for liver disease and introduces the AI-aided diagnosis of liver tumors using B-mode ultrasonography.

Artificial Intelligence in Hepatology- Ready for the Primetime

Artificial Intelligence (AI) is a mathematical process of computer mediating designing of algorithms to support human intelligence. AI in hepatology has shown tremendous promise to plan appropriate management and hence improve treatment outcomes. The field of AI is in a very early phase with limited clinical use. AI tools such as machine learning, deep learning, and 'big data' are in a continuous phase of evolution, presently being applied for clinical and basic research. In this review, we have summarized various AI applications in hepatology, the pitfalls and AI's future implications. Different AI models and algorithms are under study using clinical, laboratory, endoscopic and imaging parameters to diagnose and manage liver diseases and mass lesions. AI has helped to reduce human errors and improve treatment protocols. Further research and validation are required for future use of AI in hepatology.

Artificial intelligence in nonalcoholic fatty liver disease

Background

Nonalcoholic fatty liver disease (NAFLD) has led to serious health-related complications worldwide. NAFLD has wide pathological spectra, ranging from simple steatosis to hepatitis to cirrhosis and hepatocellular carcinoma. Artificial intelligence (AI), including machine learning and deep learning algorithms, has provided great advancement and accuracy in identifying, diagnosing, and managing patients with NAFLD and detecting squeal such as advanced fibrosis and risk factors for hepatocellular cancer. This review summarizes different AI algorithms and methods in the field of hepatology, focusing on NAFLD.

Methods

A search of PubMed, WILEY, and MEDLINE databases were taken as relevant publications for this review on the application of AI techniques in detecting NAFLD in suspected population

Results

Out of 495 articles searched in relevant databases, 49 articles were finally included and analyzed. NASH-Scope model accurately distinguished between NAFLD and non-NAFLD and between NAFLD without fibrosis and NASH with fibrosis. The logistic regression (LR) model had the highest accuracy, whereas the support vector machine (SVM) had the highest specificity and precision in diagnosing NAFLD. An extreme gradient boosting model had the highest performance in predicting non-alcoholic steatohepatitis (NASH). Electronic health record (EHR) database studies helped the diagnose NAFLD/NASH. Automated image analysis techniques predicted NAFLD severity. Deep learning radiomic elastography (DLRE) had perfect accuracy in diagnosing the cases of advanced fibrosis.

Conclusion

AI in NAFLD has streamlined specific patient identification and has eased assessment and management methods of patients with NAFLD.

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.

Fat mass index as a screening tool for the assessment of non-alcoholic fatty liver disease

Non-alcoholic fatty liver disease (NAFLD) is replacing hepatitis B as the leading cause of chronic liver disease in China. The purpose of this study is to select good tools to identify NAFLD from the body composition, anthropometry and related routine clinical parameters. A total of 5076 steelworkers, aged 22-60 years, was included in this study. Body fat mass was measured via bioelectrical impedance analysis (BIA) and fat mass index (FMI) was derived. Ultrasonography method was used to detect hepatic steatosis. Random forest classifier and best subset regression were used to select useful parameters or models that can accurately identify NAFLD. Receiver operating characteristic (ROC) curves were used to describe and compare the performance of different diagnostic indicators and algorithms including fatty liver index (FLI) and hepatic steatosis index (HSI) in NAFLD screening. ROC analysis indicated that FMI can be used with high accuracy to identify heavy steatosis as determined by ultrasonography in male workers [area under the curve (AUC) 0.95, 95% CI 0.93-0.98, sensitivity 89.0%, specificity 91.4%]. The ability of single FMI to identify NAFLD is no less than that of combination panels, even better than the combination panel of HSI. The best subset regression model that including FMI, waist circumference, and serum levels of triglyceride and alanine aminotransferase has moderate accuracy in diagnosing overall NAFLD (AUC 0.83). FMI and the NAFLD best subset (BIC) score seem to be good tools to identify NAFLD in Chinese steelworkers.

Risk of liver-related events by age and diabetes duration in patients with diabetes and nonalcoholic fatty liver disease

BACKGROUND AND AIMS

Several guidelines recommend screening for NAFLD in patients with type 2 diabetes (T2D). We aimed to determine if there is a threshold of age and duration of T2D for liver-related event development to guide screening strategies.

APPROACH AND RESULTS

We conducted a territory-wide retrospective cohort study of adult patients with NAFLD and T2D diagnosed between 2000 and 2014 in Hong Kong to allow for at least 5 years of follow-up. The primary endpoint was liver-related events, defined as a composite of HCC and cirrhotic complications. This study included 7028 patients with NAFLD with T2D (mean age, 56.1 ± 13.3 years; 3363 male [47.9%]). During a follow-up of 77,308 person-years, there was a threshold effect with 1.1%, 4.9%, and 94.0% of patients developing liver-related events at the age of <40, 40-50, and ≥50 years, respectively. Similarly, 3.1%, 5.1%, and 91.8% of patients developed cirrhosis at the age of <40, 40-50, and ≥50 years, respectively. In contrast, liver-related events increased linearly with diabetes duration, with no difference in the annual incidence rate between the first 10 years of T2D diagnosis and subsequent years (0.06% vs. 0.10%; p = 0.136). On multivariable analysis, baseline age ≥50 years (adjusted HR [aHR] 2.01) and cirrhosis (aHR 3.12) were the strongest risk factors associated with liver-related events. Substitution of cirrhosis with the aspartate aminotransferase-to-platelet ratio index or the Fibrosis-4 index yielded similar results.

CONCLUSIONS

Age rather than duration of T2D predicts liver-related events in patients with NAFLD and T2D. It is reasonable to screen patients with NAFLD and T2D for advanced liver disease starting at 50 years of age.

A Deep Learning-Based Model for Predicting Abnormal Liver Function in Workers in the Automotive Manufacturing Industry: A Cross-Sectional Survey in Chongqing, China

To identify the influencing factors and develop a predictive model for the risk of abnormal liver function in the automotive manufacturing industry works in Chongqing. Automotive manufacturing workers in Chongqing city surveyed during 2019-2021 were used as the study subjects. Logistic regression analysis was used to identify the influencing factors of abnormal liver function. A restricted cubic spline model was used to further explore the influence of the length of service. Finally, a deep neural network-based model for predicting the risk of abnormal liver function among workers was developed. Of all 6087 study subjects, a total of 1018 (16.7%) cases were detected with abnormal liver function. Increased BMI, length of service, DBP, SBP, and being male were independent risk factors for abnormal liver function. The risk of abnormal liver function rises sharply with increasing length of service below 10 years. AUC values of the model were 0.764 (95% CI: 0.746-0.783) and 0.756 (95% CI: 0.727-0.786) in the training and test sets, respectively. The other four evaluation indices of the DNN model also achieved good values.

A machine-learning approach for nonalcoholic steatohepatitis susceptibility estimation

BACKGROUND

Nonalcoholic steatohepatitis (NASH), a severe form of nonalcoholic fatty liver disease, can lead to advanced liver damage and has become an increasingly prominent health problem worldwide. Predictive models for early identification of high-risk individuals could help identify preventive and interventional measures. Traditional epidemiological models with limited predictive power are based on statistical analysis. In the current study, a novel machine-learning approach was developed for individual NASH susceptibility prediction using candidate single nucleotide polymorphisms (SNPs).

METHODS

A total of 245 NASH patients and 120 healthy individuals were included in the study. Single nucleotide polymorphism genotypes of candidate genes including two SNPs in the cytochrome P450 family 2 subfamily E member 1 (CYP2E1) gene (rs6413432, rs3813867), two SNPs in the glucokinase regulator (GCKR) gene (rs780094, rs1260326), rs738409 SNP in patatin-like phospholipase domain-containing 3 (PNPLA3), and gender parameters were used to develop models for identifying at-risk individuals. To predict the individual's susceptibility to NASH, nine different machine-learning models were constructed. These models involved two different feature selections including Chi-square, and support vector machine recursive feature elimination (SVM-RFE) and three classification algorithms including k-nearest neighbor (KNN), multi-layer perceptron (MLP), and random forest (RF). All nine machine-learning models were trained using 80% of both the NASH patients and the healthy controls data. The nine machine-learning models were then tested on 20% of both groups. The model's performance was compared for model accuracy, precision, sensitivity, and F measure.

RESULTS

Among all nine machine-learning models, the KNN classifier with all features as input showed the highest performance with 86% F measure and 79% accuracy.

CONCLUSIONS

Machine learning based on genomic variety may be applicable for estimating an individual's susceptibility for developing NASH among high-risk groups with a high degree of accuracy, precision, and sensitivity.

Using machine learning models to predict HBeAg seroconversion in CHB patients receiving pegylated interferon-α monotherapy

BACKGROUND AND OBJECTIVE

Though there are many advantages of pegylated interferon-α (PegIFN-α) treatment to chronic hepatitis B (CHB) patients, the response rate of PegIFN-α is only 30 ~ 40%. Therefore, it is important to explore predictors at baseline and establish models to improve the response rate of PegIFN-α.

METHODS

We randomly divided 260 HBeAg-positive CHB patients who were not previously treated and received PegIFN-α monotherapy (180 μg/week) into a training dataset (70%) and testing dataset (30%). The intersect features were extracted from 50 routine laboratory variables using the recursive feature elimination method algorithm, Boruta algorithm, and Least Absolute Shrinkage and Selection Operator Regression algorithm in the training dataset. After that, based on the intersect features, eight machine learning models including Logistic Regression, k-Nearest Neighbors, Support Vector Machine, Decision Tree, Random Forest, Gradient Boosting, Extreme Gradient Boosting (XGBoost), and Naïve Bayes were applied to evaluate HBeAg seroconversion in HBeAg-positive CHB patients receiving PegIFN-α monotherapy in the training dataset and testing dataset.

RESULTS

XGBoost model showed the best performance, which had largest AUROC (0.900, 95% CI: 0.85-0.95 and 0.910, 95% CI: 0.84-0.98, in training dataset and testing dataset, respectively), and the best calibration curve performance to predict HBeAg seroconversion. The importance of XGBoost model indicated that treatment time contributed greatest to HBeAg seroconversion, followed by HBV DNA(log), HBeAg, HBeAb, HBcAb, ALT, triglyceride, and ALP.

CONCLUSIONS

XGBoost model based on common laboratory variables had good performance in predicting HBeAg seroconversion in HBeAg-positive CHB patients receiving PegIFN-α monotherapy.

Angiotensin-converting enzyme inhibitors prevent liver-related events in nonalcoholic fatty liver disease

BACKGROUND AND AIMS

Angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) can inhibit liver fibrogenesis in animal models. We aimed to evaluate the impact of ACEI/ARB use on the risk of liver cancer and cirrhosis complications in patients with NAFLD.

APPROACH AND RESULTS

We conducted a retrospective, territory-wide cohort study of adult patients with NAFLD diagnosed between January 2000 and December 2014 to allow for at least 5 years of follow-up. ACEI or ARB users were defined as patients who had received ACEI or ARB treatment for at least 6 months. The primary endpoint was liver-related events (LREs), defined as a composite endpoint of liver cancer and cirrhosis complications. We analyzed data from 12,327 NAFLD patients (mean age, 54.2 ± 14.7 years; 6163 men [50.0%]); 6805 received ACEIs, and 2877 received ARBs. After propensity score weighting, ACEI treatment was associated with a lower risk of LREs (weighted subdistribution hazard ratio [SHR], 0.48; 95% CI, 0.35-0.66; p < 0.001), liver cancer (weighted SHR, 0.46; 95% CI, 0.28-0.75; p = 0.002), and cirrhosis complications (weighted SHR, 0.42; 95% CI, 0.27-0.66; p < 0.001), but ARB was not. In subgroup analysis, ACEI treatment was associated with greater reduction in LREs in patients with chronic kidney diseases (CKDs) than those without (CKD-weighted SHR, 0.74; 95% CI, 0.52-0.96; p = 0.036; non-CKD-weighted SHR, 0.15; 95% CI, 0.07-0.33; p < 0.001).

CONCLUSIONS

ACEI, rather than ARB, treatment is associated with a lower risk of LREs in NAFLD patients, especially among those with CKD.

Identification of hepatic steatosis in living liver donors by machine learning models

Selecting an optimal donor for living donor liver transplantation is crucial for the safety of both the donor and recipient, and hepatic steatosis is an important consideration. We aimed to build a prediction model with noninvasive variables to evaluate macrovesicular steatosis in potential donors by using various prediction models. The study population comprised potential living donors who had undergone donation workup, including percutaneous liver biopsy, in the Republic of Korea between 2016 and 2019. Meaningful macrovesicular hepatic steatosis was defined as >5%. Whole data were divided into training (70.5%) and test (29.5%) data sets based on the date of liver biopsy. Random forest, support vector machine, regularized discriminant analysis, mixture discriminant analysis, flexible discriminant analysis, and deep neural network machine learning methods as well as traditional logistic regression were employed. The mean patient age was 31.4 years, and 66.3% of the patients were men. Of the 1652 patients, 518 (31.4%) had >5% macrovesicular steatosis on the liver biopsy specimen. The logistic model had the best prediction power and prediction performances with an accuracy of 80.0% and 80.9% in the training and test data sets, respectively. A cut-off value of 31.1% for the predicted risk of hepatic steatosis was selected with a sensitivity of 77.7% and specificity of 81.0%. We have provided our model on the website (https://hanseungbong.shinyapps.io/shiny_app_up/) under the name DONATION Model. Our algorithm to predict macrovesicular steatosis using routine parameters is beneficial for identifying optimal potential living donors by avoiding superfluous liver biopsy results.

Machine learning approaches using blood biomarkers in non-alcoholic fatty liver diseases

The prevalence of nonalcoholic fatty liver disease (NAFLD) is an important public health concern. Early diagnosis of NAFLD and potential progression to nonalcoholic steatohepatitis (NASH), could reduce the further advance of the disease, and improve patient outcomes. Aiming to support patient diagnostic and predict specific outcomes, the interest in artificial intelligence (AI) methods in hepatology has dramatically increased, especially with the application of less-invasive biomarkers. In this review, our objective was twofold: Firstly, we presented the most frequent blood biomarkers in NAFLD and NASH and secondly, we reviewed recent literature regarding the use of machine learning (ML) methods to predict NAFLD and NASH in large cohorts. Strikingly, these studies provide insights into ML application in NAFLD patients' prognostics and ranked blood biomarkers are able to provide a recognizable signature allowing cost-effective NAFLD prediction and also differentiating NASH patients. Future studies should consider the limitations in the current literature and expand the application of these algorithms in different populations, fortifying an already promising tool in medical science.

Applications of artificial intelligence (AI) in researches on non-alcoholic fatty liver disease(NAFLD) : A systematic review

Non-alcoholic fatty liver disease (NAFLD) is one of the most important causes of chronic liver disease in the world, it has been found that cardiovascular and renal risks and diseases are also highly prevalent in adults with NAFLD. Diagnosis and treatment of NAFLD face many challenges, although the medical science has been very developed. Efficiency, accuracy and individualization are the main goals to be solved. Evaluation of the severity of NAFLD involves a variety of clinical parameters, how to optimize non-invasive evaluation methods is a necessary issue that needs to be discussed in this field. Artificial intelligence (AI) has become increasingly widespread in healthcare applications, and it has been also brought many new insights into better analyzing chronic liver disease, including NAFLD. This paper reviewed AI related researches in NAFLD field published recently, summarized diagnostic models based on electronic health record and lab test, ultrasound and radio imaging, and liver histopathological data, described the application of therapeutic models in personalized lifestyle guidance and the development of drugs for NAFLD. In addition, we also analyzed present AI models in distinguishing healthy VS NAFLD/NASH, and fibrosis VS non-fibrosis in the evaluation of NAFLD progression. We hope to provide alternative directions for the future research.

A Machine Learning Based Framework to Identify and Classify Non-alcoholic Fatty Liver Disease in a Large-Scale Population

Non-alcoholic fatty liver disease (NAFLD) is a common serious health problem worldwide, which lacks efficient medical treatment. We aimed to develop and validate the machine learning (ML) models which could be used to the accurate screening of large number of people. This paper included 304,145 adults who have joined in the national physical examination and used their questionnaire and physical measurement parameters as model's candidate covariates. Absolute shrinkage and selection operator (LASSO) was used to feature selection from candidate covariates, then four ML algorithms were used to build the screening model for NAFLD, used a classifier with the best performance to output the importance score of the covariate in NAFLD. Among the four ML algorithms, XGBoost owned the best performance (accuracy = 0.880, precision = 0.801, recall = 0.894, F-1 = 0.882, and AUC = 0.951), and the importance ranking of covariates is accordingly BMI, age, waist circumference, gender, type 2 diabetes, gallbladder disease, smoking, hypertension, dietary status, physical activity, oil-loving and salt-loving. ML classifiers could help medical agencies achieve the early identification and classification of NAFLD, which is particularly useful for areas with poor economy, and the covariates' importance degree will be helpful to the prevention and treatment of NAFLD.

The digital transformation of hepatology: The patient is logged in

The rise in innovative digital health technologies has led a paradigm shift in health care toward personalized, patient-centric medicine that is reaching beyond traditional brick-and-mortar facilities into patients' homes and everyday lives. Digital solutions can monitor and detect early changes in physiological data, predict disease progression and health-related outcomes based on individual risk factors, and manage disease intervention with a range of accessible telemedicine and mobile health options. In this review, we discuss the unique transformation underway in the care of patients with liver disease, specifically examining the digital transformation of diagnostics, prediction and clinical decision-making, and management. Additionally, we discuss the general considerations needed to confirm validity and oversight of new technologies, usability and acceptability of digital solutions, and equity and inclusivity of vulnerable populations.

The application of artificial intelligence in hepatology: A systematic review

The integration of human and artificial intelligence (AI) in medicine has only recently begun but it has already become obvious that intelligent systems can dramatically improve the management of liver diseases. Big data made it possible to envisage transformative developments of the use of AI for diagnosing, predicting prognosis and treating liver diseases, but there is still a lot of work to do. If we want to achieve the 21^st century digital revolution, there is an urgent need for specific national and international rules, and to adhere to bioethical parameters when collecting data. Avoiding misleading results is essential for the effective use of AI. A crucial question is whether it is possible to sustain, technically and morally, the process of integration between man and machine. We present a systematic review on the applications of AI to hepatology, highlighting the current challenges and crucial issues related to the use of such technologies.

MAFLD/NAFLD Biopsy-Free Scoring Systems for Hepatic Steatosis, NASH, and Fibrosis Diagnosis

Metabolic dysfunction-associated fatty liver disease (MAFLD), formerly known as nonalcoholic fatty liver disease, is the most prevalent liver disorder worldwide. Historically, its diagnosis required biopsy, even though the procedure has a variable degree of error. Therefore, new non-invasive strategies are needed. Consequently, this article presents a thorough review of biopsy-free scoring systems proposed for the diagnosis of MAFLD. Similarly, it compares the severity of the disease, ranging from hepatic steatosis (HS) and nonalcoholic steatohepatitis (NASH) to fibrosis, by contrasting the corresponding serum markers, clinical associations, and performance metrics of these biopsy-free scoring systems. In this regard, defining MAFLD in conjunction with non-invasive tests can accurately identify patients with fatty liver at risk of fibrosis and its complications. Nonetheless, several biopsy-free scoring systems have been assessed only in certain cohorts; thus, further validation studies in different populations are required, with adjustment for variables, such as body mass index (BMI), clinical settings, concomitant diseases, and ethnic backgrounds. Hence, comprehensive studies on the effects of age, morbid obesity, and prevalence of MAFLD and advanced fibrosis in the target population are required. Nevertheless, the current clinical practice is urged to incorporate biopsy-free scoring systems that demonstrate adequate performance metrics for the accurate detection of patients with MAFLD and underlying conditions or those with contraindications of biopsy.

Non-invasive tests of non-alcoholic fatty liver disease

ABSTRACT

For the detection of steatosis, quantitative ultrasound imaging techniques have achieved great progress in past years. Magnetic resonance imaging proton density fat fraction is currently the most accurate test to detect hepatic steatosis. Some blood biomarkers correlate with non-alcoholic steatohepatitis, but the accuracy is modest. Regarding liver fibrosis, liver stiffness measurement by transient elastography (TE) has high accuracy and is widely used across the world. Magnetic resonance elastography is marginally better than TE but is limited by its cost and availability. Several blood biomarkers of fibrosis have been used in clinical trials and hold promise for selecting patients for treatment and monitoring treatment response. This article reviews new developments in the non-invasive assessment of non-alcoholic fatty liver disease (NAFLD). Accumulating evidence suggests that various non-invasive tests can be used to diagnose NAFLD, assess its severity, and predict the prognosis. Further studies are needed to determine the role of the tests as monitoring tools. We cannot overemphasize the importance of context in selecting appropriate tests.

Low awareness of non-alcoholic fatty liver disease in patients with type 2 diabetes in Swedish Primary Health Care

OBJECTIVES

Non-alcoholic fatty liver disease (NAFLD) is more common in patients with type 2 diabetes mellitus (T2DM) compared to individuals without. Recent guidelines recommend screening for NAFLD in patients with T2DM. Our aim was to investigate the prevalence of NAFLD in patients with T2DM in a Swedish primary health care setting, how they are cared for and assess the risk of biochemical signs of advanced fibrosis.

MATERIAL AND METHODS

In this cohort study, patients with T2DM from five primary health care centers were included. Medical records were retrospectively reviewed and living habits, medical history, results of diagnostic imaging and anthropometric and biochemical features were noted in a standardized form. The risk of steatosis and advanced fibrosis was assessed using commonly used algorithms (FLI, HSI, NAFLD-LFS, NAFLD ridge score, FIB-4 and NFS).

RESULTS

In total 350 patients were included. Diagnostic imaging had been performed in 132 patients and of these, 34 (26%) had steatosis, which was not noted in the medical records in 16 (47%) patients. One patient with steatosis had been referred to a hepatologist. Of assessable patients, 71-97% had a high to intermediate risk of steatosis and 29-65% had an intermediate to high risk of advanced fibrosis according to the algorithms used.

CONCLUSION

This study indicates a high prevalence of NAFLD among T2DM patients in Swedish primary care. Patients with known NAFLD were followed up to a very low extent. Using fibrosis algorithms in primary health care would result in many patients needing further assessment in secondary care.

Non-invasive assessment of metabolic dysfunction-associated fatty liver disease

Non-alcoholic fatty liver disease (NAFLD) affects an estimated one-quarter of the global adult population and has become one of the leading causes of end-stage liver disease and hepatocellular carcinoma with increased liver-related and overall morbidity and mortality. The new term, metabolic dysfunction-associated fatty liver disease (MAFLD), has a set of positive diagnostic criteria and has been shown to have better clinical utility, but it has yet to be universally adopted. This review addresses the non-invasive tests for MAFLD and is based mostly on studies on NAFLD patients, as the MAFLD term is relatively new and there are limited studies on non-invasive tests based on this new term, while a large body of research work on non-invasive tests has accumulated in the literature for NAFLD. This review focuses on blood-based biomarkers and scores for the assessment of hepatic steatosis, non-alcoholic steatohepatitis (NASH), and fibrosis, and two of the most widely studied imaging biomarkers, namely vibration-controlled transient elastography and magnetic resonance imaging. Fibrotic NASH has become a diagnostic target of interest and novel serum biomarkers and scores incorporating imaging biomarker for diagnosis of fibrotic NASH are emerging. Nonetheless, the degree of liver fibrosis remains the key predictor of liver-related morbidity and mortality in patients with MAFLD. A multitude of non-invasive biomarkers and scores have been studied for the detection of liver fibrosis, including use of sequential non-invasive tests for risk stratification of advanced liver fibrosis. In addition, this review will explore the utility of the non-invasive tests for prognostication and for monitoring of treatment response.

Advances in Predictive Modeling Using Machine Learning in the Field of Hepatology

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