Artificial intelligence/neural network system for the screening of nonalcoholic fatty liver disease and nonalcoholic steatohepatitis

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.

Machine learning improves the prediction of significant fibrosis in Asian patients with metabolic dysfunction-associated steatotic liver disease - The Gut and Obesity in Asia (GO-ASIA) Study

BACKGROUND

The precise estimation of cases with significant fibrosis (SF) is an unmet goal in non-alcoholic fatty liver disease (NAFLD/MASLD).

AIMS

We evaluated the performance of machine learning (ML) and non-patented scores for ruling out SF among NAFLD/MASLD patients.

METHODS

Twenty-one ML models were trained (N = 1153), tested (N = 283), and validated (N = 220) on clinical and biochemical parameters of histologically-proven NAFLD/MASLD patients (N = 1656) collected across 14 centres in 8 Asian countries. Their performance for detecting histological-SF (≥F2fibrosis) were evaluated with APRI, FIB4, NFS, BARD, and SAFE (NPV/F1-score as model-selection criteria).

RESULTS

Patients aged 47 years (median), 54.6% males, 73.7% with metabolic syndrome, and 32.9% with histological-SF were included in the study. Patients with SFvs.no-SF had higher age, aminotransferases, fasting plasma glucose, metabolic syndrome, uncontrolled diabetes, and NAFLD activity score (p < 0.001, each). ML models showed 7%-12% better discrimination than FIB-4 to detect SF. Optimised random forest (RF) yielded best NPV/F1 in overall set (0.947/0.754), test set (0.798/0.588) and validation set (0.852/0.559), as compared to FIB4 in overall set (0.744/0.499), test set (0.722/0.456), and validation set (0.806/0.507). Compared to FIB-4, RF could pick 10 times more patients with SF, reduce unnecessary referrals by 28%, and prevent missed referrals by 78%. Age, AST, ALT fasting plasma glucose, and platelet count were top features determining the SF. Sequential use of SAFE < 140 and FIB4 < 1.2 (when SAFE > 140) was next best in ruling out SF (NPV of 0.757, 0.724 and 0.827 in overall, test and validation set).

CONCLUSIONS

ML with clinical, anthropometric data and simple blood investigations perform better than FIB-4 for ruling out SF in biopsy-proven Asian NAFLD/MASLD patients.

Risk Stratification of Advanced Fibrosis in Patients With Human Immunodeficiency Virus and Hepatic Steatosis Using the Fibrosis-4, Nonalcoholic Fatty Liver Disease Fibrosis, and BARD Scores

Background

Nonalcoholic fatty liver disease (NAFLD) and subsequent progression to fibrosis is increasingly prevalent in people with HIV (PWH). We used noninvasive methods to stratify risk and identify associated factors of advanced fibrosis in PWH with NAFLD.

Methods

We conducted a retrospective study of PWH in our clinic from 2005 to 2022. We used liver imaging or biopsy reports to identify cases of hepatic steatosis after excluding specified etiologies. We used the Fibrosis-4 (FIB-4), NAFLD Fibrosis (NFS), and body mass index, aspartate transaminase/alanine transaminase ratio, and diabetes score scores to stratify fibrosis. We used logistic regression to identify factors associated with advanced fibrosis.

Results

Among 3959 PWH in care, 1201 had available imaging or liver biopsies. After exclusions, 114 of 783 PWH had evidence of hepatic steatosis (14.6%). Most were male (71.1%), with a median age of 47 years, and median body mass index of 30.1 kg/m2. Approximately 24% had lean NAFLD (ie, body mass index < 25 kg/m2). Based on the FIB-4 and NFS, 34 (29.8%) and 36 (31.6%) had advanced fibrosis, whereas 1 in 4 had low risk of fibrosis based on FIB-4, NFS, and BARD scores. In adjusted analysis using FIB-4, advanced fibrosis was associated with age > 45 years (adjusted odds ratio, 6.29; 95% confidence interval, 1.93-20.50) and hypoalbuminemia (adjusted odds ratio, 9.45; 95% confidence interval, 2.45-32.52) in addition to elevated transaminases and thrombocytopenia, whereas using the NFS did not identify associations with advanced fibrosis.

Conclusions

We found 14.6% of PWH had NAFLD, with 1 in 3 having advanced fibrosis. Our study provides practical insights into fibrosis risk stratification in HIV primary care settings.

Application of artificial intelligence techniques for non-alcoholic fatty liver disease diagnosis: A systematic review (2005-2023)

BACKGROUND AND OBJECTIVES

Non-alcoholic fatty liver disease (NAFLD) is a common liver disease with a rapidly growing incidence worldwide. For prognostication and therapeutic decisions, it is important to distinguish the pathological stages of NAFLD: steatosis, steatohepatitis, and liver fibrosis, which are definitively diagnosed on invasive biopsy. Non-invasive ultrasound (US) imaging, including US elastography technique, and clinical parameters can be used to diagnose and grade NAFLD and its complications. Artificial intelligence (AI) is increasingly being harnessed for developing NAFLD diagnostic models based on clinical, biomarker, or imaging data. In this work, we systemically reviewed the literature for AI-enabled NAFLD diagnostic models based on US (including elastography) and clinical (including serological) data.

METHODS

We performed a comprehensive search on Google Scholar, Scopus, and PubMed search engines for articles published between January 2005 and June 2023 related to AI models for NAFLD diagnosis based on US and/or clinical parameters using the following search terms: "non-alcoholic fatty liver disease", "non-alcoholic steatohepatitis", "deep learning", "machine learning", "artificial intelligence", "ultrasound imaging", "sonography", "clinical information".

RESULTS

We reviewed 64 published models that used either US (including elastography) or clinical data input to detect the presence of NAFLD, non-alcoholic steatohepatitis, and/or fibrosis, and in some cases, the severity of steatosis, inflammation, and/or fibrosis as well. The performances of the published models were summarized, and stratified by data input and algorithms used, which could be broadly divided into machine and deep learning approaches.

CONCLUSION

AI models based on US imaging and clinical data can reliably detect NAFLD and its complications, thereby reducing diagnostic costs and the need for invasive liver biopsy. The models offer advantages of efficiency, accuracy, and accessibility, and serve as virtual assistants for specialists to accelerate disease diagnosis and reduce treatment costs for patients and healthcare systems.

Machine learning approaches for early detection of non-alcoholic steatohepatitis based on clinical and blood parameters

This study aims to develop a machine learning approach leveraging clinical data and blood parameters to predict non-alcoholic steatohepatitis (NASH) based on the NAFLD Activity Score (NAS). Using a dataset of 181 patients, we performed preprocessing including normalization and categorical encoding. To identify predictive features, we applied sequential forward selection (SFS), chi-square, analysis of variance (ANOVA), and mutual information (MI). The selected features were used to train machine learning classifiers including SVM, random forest, AdaBoost, LightGBM, and XGBoost. Hyperparameter tuning was done for each classifier using randomized search. Model evaluation was performed using leave-one-out cross-validation over 100 repetitions. Among the classifiers, random forest, combined with SFS feature selection and 10 features, obtained the best performance: Accuracy: 81.32% ± 6.43%, Sensitivity: 86.04% ± 6.21%, Specificity: 70.49% ± 8.12% Precision: 81.59% ± 6.23%, and F1-score: 83.75% ± 6.23% percent. Our findings highlight the promise of machine learning in enhancing early diagnosis of NASH and provide a compelling alternative to conventional diagnostic techniques. Consequently, this study highlights the promise of machine learning techniques in enhancing early and non-invasive diagnosis of NASH based on readily available clinical and blood data. Our findings provide the basis for developing scalable approaches that can improve screening and monitoring of NASH progression.

Early prediction of atherosclerosis diagnosis with medical ambient intelligence

Atherosclerosis is a chronic vascular disease that poses a significant threat to human health. Common diagnostic methods mainly rely on active screening, which often misses the opportunity for early detection. To overcome this problem, this paper presents a novel medical ambient intelligence system for the early detection of atherosclerosis by leveraging clinical data from medical records. The system architecture includes clinical data extraction, transformation, normalization, feature selection, medical ambient computation, and predictive generation. However, the heterogeneity of examination items from different patients can degrade prediction performance. To enhance prediction performance, the "SEcond-order Classifier (SEC)" is proposed to undertake the medical ambient computation task. The first-order component and second-order cross-feature component are then consolidated and applied to the chosen feature matrix to learn the associations between the physical examination data, respectively. The prediction is lastly produced by aggregating the representations. Extensive experimental results reveal that the proposed method's diagnostic prediction performance is superior to other state-of-the-art methods. Specifically, the Vitamin B12 indicator exhibits the strongest correlation with the early stage of atherosclerosis, while several known relevant biomarkers also demonstrate significant correlation in experimental data. The method proposed in this paper is a standalone tool, and its source code will be released in the future.

Risk Stratification of Advanced Fibrosis in HIV Patients With Hepatic Steatosis Using the NAFLD Fibrosis and BARD Scores

Background

Nonalcoholic fatty liver disease (NAFLD) is increasingly prevalent in people with HIV (PWH), yet the risk factors for disease progression are poorly understood, due to inadequate surveillance. We employed non-invasive methods to estimate the prevalence and associated factors of advanced NAFLD in PWH.

Methods

We conducted a retrospective study of PWH enrolled in our clinic from 2005 to 2022. We employed imaging (ultrasound, computer tomography, magnetic resonance imaging, and transient elastography) or biopsy reports to identify cases of hepatic steatosis. We excluded patients with harmful alcohol use, hepatitis B or C infection, and other specified etiologies. We used the NAFLD Fibrosis Score (NFS), BARD Score, AST to Platelet Index (APRI), and Fibrosis-4 (FIB-4) Score to stratify fibrosis. We used logistic regression to identify predictors of advanced fibrosis.

Results

Among 3959 PWH in care, 1201 had available imaging or liver biopsies. After exclusions, 114 of the remaining 783 had evidence of hepatic steatosis (prevalence 14.6%). The majority were male (71.1%), with mean age 46.1 years, and mean body mass index (BMI) 31.4 ± 8.1 kg/m2. About 24% had lean NAFLD (BMI < 25 kg/m2). Based on the NFS, 27.2% had advanced fibrosis, which was corroborated by estimates from the other scores. In adjusted regression analysis, advanced fibrosis was associated with BMI > 35 kg/m2 (4.43, 1.27-15.48), thrombocytopenia (4.85, 1.27-18.62) and hypoalbuminemia (9.01, 2.39-33.91).

Conclusion

We found a NAFLD prevalence of 14.6%, with 27.2% of cases having advanced fibrosis. Our study provides practical insights into the surveillance of NAFLD in PWH.

Ultrasound-Based Machine Learning Approach for Detection of Nonalcoholic Fatty Liver Disease

OBJECTIVES

Current diagnosis of nonalcoholic fatty liver disease (NAFLD) relies on biopsy or MR-based fat quantification. This prospective study explored the use of ultrasound with artificial intelligence for the detection of NAFLD.

METHODS

One hundred and twenty subjects with clinical suspicion of NAFLD and 10 healthy volunteers consented to participate in this institutional review board-approved study. Subjects were categorized as NAFLD and non-NAFLD according to MR proton density fat fraction (PDFF) findings. Ultrasound images from 10 different locations in the right and left hepatic lobes were collected following a standard protocol. MRI-based liver fat quantification was used as the reference standard with >6.4% indicative of NAFLD. A supervised machine learning model was developed for assessment of NAFLD. To validate model performance, a balanced testing dataset of 24 subjects was used. Sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy with 95% confidence interval were calculated.

RESULTS

A total of 1119 images from 106 participants was used for model development. The internal evaluation achieved an average precision of 0.941, recall of 88.2%, and precision of 89.0%. In the testing set AutoML achieved a sensitivity of 72.2% (63.1%-80.1%), specificity of 94.6% (88.7%-98.0%), positive predictive value (PPV) of 93.1% (86.0%-96.7%), negative predictive value of 77.3% (71.6%-82.1%), and accuracy of 83.4% (77.9%-88.0%). The average agreement for an individual subject was 92%.

CONCLUSIONS

An ultrasound-based machine learning model for identification of NAFLD showed high specificity and PPV in this prospective trial. This approach may in the future be used as an inexpensive and noninvasive screening tool for identifying NAFLD in high-risk patients.

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.

SWOT analysis of noninvasive tests for diagnosing NAFLD with severe fibrosis: an expert review by the JANIT Forum

Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease. Nonalcoholic steatohepatitis (NASH) is an advanced form of NAFLD can progress to liver cirrhosis and hepatocellular carcinoma (HCC). Recently, the prognosis of NAFLD/NASH has been reported to be dependent on liver fibrosis degree. Liver biopsy remains the gold standard, but it has several issues that must be addressed, including its invasiveness, cost, and inter-observer diagnosis variability. To solve these issues, a variety of noninvasive tests (NITs) have been in development for the assessment of NAFLD progression, including blood biomarkers and imaging methods, although the use of NITs varies around the world. The aim of the Japan NASH NIT (JANIT) Forum organized in 2020 is to advance the development of various NITs to assess disease severity and/or response to treatment in NAFLD patients from a scientific perspective through multi-stakeholder dialogue with open innovation, including clinicians with expertise in NAFLD/NASH, companies that develop medical devices and biomarkers, and professionals in the pharmaceutical industry. In addition to conventional NITs, artificial intelligence will soon be deployed in many areas of the NAFLD landscape. To discuss the characteristics of each NIT, we conducted a SWOT (strengths, weaknesses, opportunities, and threats) analysis in this study with the 36 JANIT Forum members (16 physicians and 20 company representatives). Based on this SWOT analysis, the JANIT Forum identified currently available NITs able to accurately select NAFLD patients at high risk of NASH for HCC surveillance/therapeutic intervention and evaluate the effectiveness of therapeutic interventions.

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.

Artificial intelligence model with deep learning in nonalcoholic fatty liver disease diagnosis: genetic based artificial neural networks

Nonalcoholic fatty liver disease (NAFLD) is one of the most common causes of chronic liver disease in the world. The NAFLD spectrum includes simple steatosis, steatohepatitis, fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). Genetic, nutritional factors, obesity, insulin resistance, gut microbiota are among the risk factors for NAFLD. The genetic variant Patatin-like phospholipase domain-containing protein 3 (PNPLA3) plays an important role in the development of a number of liver diseases ranging from steatosis, chronic hepatitis, cirrhosis and HCC. Due to the increase in the prevalence of NAFLD, new models are being developed with machine learning, deep learning, artificial neural network (ANN) algorithms in the field of artificial intelligence (AI) to determine low-cost, noninvasive diagnostic methods. Models developed with ANN from AI modules are important in order to examine biochemical and genomic information in detail in the diagnosis of NAFLD. The aim of this study is to develop a simple ANN model using biochemical and genotypic parameters in the diagnosis of NAFLD. A total of 300 patients followed up with the diagnosis of NAFLD and 100 controls were included in the study. The data set was divided into two as training and test set. Genotyping of PNPLA3 (CC, CG, GG) as genomic analysis was performed with real time PCR device. The algorithm used for the diagnosis of NAFLD was designed using age, body mass index (BMI), mean platelet volume (MPV), insulin resistance (IR), alanine aminotransferase (ALT), genotype PNPLA3 (CC, CG, GG) parameters. MLP Classifier algorithm from ANN was used in the development of the model. ANN algorithms are used in python programming language. Statistical analyzes were made in SPSS program. Percent accuracy, area under the ROC curve, confusion matrix, Positive (PPV) and Negative Predicted Value (NPV) values, precision, recall, and f1-score results were determined. The accuracy percentage was determined as 0.979 in the train set and 0.970 in the test set. The Log Loss value was set to 0.09. The developed neural network achieved an accuracy percentage of 97.0% during testing, with an area under the ROC curve value of 0.95. We think that the ANN model developed with genomic and biochemical parameters can be used as a cost-effective, noninvasive new predictive diagnostic model in clinical practice in the diagnosis of NAFLD.

Comparison of thrombocytopenia between patients with non-alcoholic fatty liver disease and those with hepatitis C virus-related chronic liver disease

AIM

Thrombocytopenia is widely recognized as a simple surrogate marker of liver fibrosis in non-alcoholic fatty liver disease (NAFLD). Thrombocytopenia of NAFLD has not been compared with that of hepatitis C virus-related chronic liver disease (CLD-C). Here, we examined whether there is any difference in the platelet counts between patients with NAFLD and CLD-C and investigated the underlying mechanisms.

METHODS

A total of 760 biopsy-confirmed NAFLD and 1171 CLD-C patients were enrolled. After stratification according to the liver fibrosis stage, platelet counts between NAFLD and CLD-C patients were compared. The platelet count, spleen size, serum albumin level, serum thrombopoietin level, and immature platelet fraction (IPF) value were also compared after covariate adjustment using propensity score (PS) matching.

RESULTS

The median platelet counts (×10⁴ /μL) of NAFLD and CLD-C patients were 20.2 and 18.7 (p = 2.4 × 10^-5 ) in F1; 20.0 and 14.5 (p = 2.1 × 10^-12 ) in F2; 16.9 and 12.3 (p = 8.1 × 10^-10 ) in F3; and 11.1 and 8.1 (p = 0.02) in F4, respectively. In the F3 group, NAFLD patients had a significantly higher platelet count and significantly smaller spleen volume than CLD-C patients. Although the serum thrombopoietin levels were comparable between NAFLD and CLD-C patients, the IPF value of NAFLD patients was significantly higher than that of CLD-C patients.

CONCLUSIONS

NAFLD patients had a significantly higher platelet count than CLD-C patients following stratification according to the liver fibrosis stage. The milder hypersplenism and higher platelet production in NAFLD than CLD-C may have contributed to this difference.

Changing clinical management of NAFLD in Asia

Non-alcoholic fatty liver disease (NAFLD) has become the leading cause of chronic liver disease, affecting approximately 25% of the world's population. Recently, because of the sedentary lifestyle and overnutrition resulting from urbanisation, the burden of NAFLD has rapidly increased in many Asian countries. Currently, the prevalence of NAFLD in Asia is approximately 30%, as is the case in many Western countries. In Asia, the prevalence and presentation of NAFLD vary widely across regions because of the substantial diversity in race, socioeconomic status and living environment. Furthermore, the dual aetiology of fatty liver, particularly with viral hepatitis in Asia, makes it complex and challenging to manage. Because Asians are likely to have central adiposity and insulin resistance, approximately 7%-20% of non-obese Asians with body mass indexes of less than 25 kg/m² are estimated to have NAFLD. Accumulating evidence indicates that NAFLD is associated with various extrahepatic comorbidities such as cardiovascular disease, chronic kidney disease, malignancy, in addition to liver-specific complications. Therefore, NAFLD should be managed as a multisystem disease in conjunction with metabolic syndrome. Lifestyle modification remains the basis of NAFLD management, but few patients can achieve adequate weight loss and maintain it long term. While various pharmacological agents are in phase 3 trials for steatohepatitis, Asian patients are underrepresented in most trials. This article reviews the epidemiological trends, clinical features, optimal assessment and current management practices for NAFLD in Asia.

MAFLD: Renovation of clinical practice and disease awareness of fatty liver

Recently, international expert panels have proposed a new definition of fatty liver: metabolic dysfunction-associated fatty liver disease (MAFLD). MAFLD is not just a simple renaming of non-alcoholic fatty liver disease (NAFLD). The unique feature of MAFLD is the inclusion of metabolic dysfunctions, which are high-risk factors for events. In addition, MAFLD is independent of alcohol intake and the co-existing causes of liver disease. This new concept of MAFLD may have a widespread impact on patients, medical doctors, medical staff, and various stakeholders regarding fatty liver. Thus, MAFLD may renovate clinical practice and disease awareness of fatty liver. In this review, we introduce the definition of and rationale for MAFLD. We further describe representative cases showing how the diagnostic processes differ between MAFLD and NAFLD. We also summarize recent studies comparing MAFLD with NAFLD and discuss the impact of MAFLD on clinical trials, Japanese populations, and disease awareness.

Application of artificial intelligence in non-alcoholic fatty liver disease and liver fibrosis: a systematic review and meta-analysis

BACKGROUND

The global prevalence of non-alcoholic fatty liver disease (NAFLD) continues to rise. Non-invasive diagnostic modalities including ultrasonography and clinical scoring systems have been proposed as alternatives to liver biopsy but with limited performance. Artificial intelligence (AI) is currently being integrated with conventional diagnostic methods in the hopes of performance improvements. We aimed to estimate the performance of AI-assisted systems for diagnosing NAFLD, non-alcoholic steatohepatitis (NASH), and liver fibrosis.

METHODS

A systematic review was performed to identify studies integrating AI in the diagnosis of NAFLD, NASH, and liver fibrosis. Pooled sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and summary receiver operating characteristic curves were calculated.

RESULTS

Twenty-five studies were included in the systematic review. Meta-analysis of 13 studies showed that AI significantly improved the diagnosis of NAFLD, NASH and liver fibrosis. AI-assisted ultrasonography had excellent performance for diagnosing NAFLD, with a sensitivity, specificity, PPV, NPV of 0.97 (95% confidence interval (CI): 0.91-0.99), 0.98 (95% CI: 0.89-1.00), 0.98 (95% CI: 0.93-1.00), and 0.95 (95% CI: 0.88-0.98), respectively. The performance of AI-assisted ultrasonography was better than AI-assisted clinical data sets for the identification of NAFLD, which provided a sensitivity, specificity, PPV, NPV of 0.75 (95% CI: 0.66-0.82), 0.82 (95% CI: 0.74-0.88), 0.75 (95% CI: 0.60-0.86), and 0.82 (0.74-0.87), respectively. The area under the curves were 0.98 and 0.85 for AI-assisted ultrasonography and AI-assisted clinical data sets, respectively. AI-integrated clinical data sets had a pooled sensitivity, specificity of 0.80 (95%CI: 0.75-0.85), 0.69 (95%CI: 0.53-0.82) for identifying NASH, as well as 0.99-1.00 and 0.76-1.00 for diagnosing liver fibrosis stage F1-F4, respectively.

CONCLUSION

AI-supported systems provide promising performance improvements for diagnosing NAFLD, NASH, and identifying liver fibrosis among NAFLD patients. Prospective trials with direct comparisons between AI-assisted modalities and conventional methods are warranted before real-world implementation.

PROTOCOL REGISTRATION

PROSPERO (CRD42021230391).

Novel artificial intelligent/neural network system for staging of nonalcoholic steatohepatitis

AIM

To develop a novel noninvasive test using an artificial intelligence (AI)/neural network (NN) system (named Fibro-Scope) to determine the fibrosis stage in nonalcoholic steatohepatitis (NASH).

METHODS

Three hundred twenty-four and 110 patients with histologically diagnosed nonalcoholic fatty liver disease (NAFLD) were enrolled for training and validation studies, respectively. Two independent pathologists histologically diagnosed patients with NAFLD for the validation study. Fibro-Scope was undertaken using 12 items: age, sex, height, weight, waist circumference, aspartate aminotransferase, alanine aminotransferase, γ-glutamyl transferase, cholesterol, triglyceride, platelet count, and type 4 collagen 7s.

RESULTS

Differentiation of F0 versus F1-4 using the Fibro-Scope revealed 99.5% sensitivity, 90.9% specificity, 97.4% positive predictive value, and 98.0% negative predictive value in a training study with gray zone analysis, which was also effective in the analysis without gray zone. Discrimination was also excellent when comparing F0-1 versus F2-4 and F0-2 versus F3-4. In a validation study with gray zone analysis, differentiation of F0 from F1-4 using Fibro-Scope was also excellent. The discrimination of F0-1 from F2-4 using Fibro-Scope with gray zone analysis showed over 80% sensitivity and specificity in the histological diagnosis of both pathologists, but was lower without the gray zone analysis. The discrimination of F0-2 from F3-4 was effective in the analysis with gray zone; however, their sensitivity and specificity were slightly inferior in the analysis without gray zone.

CONCLUSIONS

Artificial intelligence/neural network algorithms termed Fibro-Scope are easy to use and can accurately differentially diagnose minimal, moderate, and advanced fibrosis. Fibro-Scope will promote rapid NASH diagnosis and facilitate diagnosing the fibrosis stage in NASH.

Nonalcoholic fatty liver disease: use of diagnostic biomarkers and modalities in clinical practice

INTRODUCTION

The global burden of liver disease is increasing, and nonalcoholic fatty liver disease (NAFLD) is among the most common chronic liver diseases in Asia, Europe, North and South America. The field of noninvasive diagnostic and their role in staging, but also predicting outcome is evolving rapidly. There is a high-unmet need to stage patients with NAFLD and to identify the subset of patients at risk of progression to end-stage liver disease.

AREAS COVERED

The review covers all established diagnostic blood-based and imaging biomarkers to stage and grade NAFLD. Noninvasive surrogate scores are put into perspective of the available evidence and recommended use. The outlook includes genetics, combined algorithms, and artificial intelligence that will allow clinicians to guide and support the management in both early and later disease stages.

EXPERT OPINION

In the future, these diagnostics tests will help clinicians to establish patient care pathways and support the identification of relevant subgroups for monitoring and pharmacotherapy. In addition, researchers will be guided to better understand available scores and support the development of future prediction systems. These will likely include multiparametric aspects of the disease and machine learning algorithms will refine their use and integration with large datasets.

Deep learning-based image-analysis algorithm for classification and quantification of multiple histopathological lesions in rat liver

Artificial intelligence (AI)-based image analysis is increasingly being used for preclinical safety-assessment studies in the pharmaceutical industry. In this paper, we present an AI-based solution for preclinical toxicology studies. We trained a set of algorithms to learn and quantify multiple typical histopathological findings in whole slide images (WSIs) of the livers of young Sprague Dawley rats by using a U-Net-based deep learning network. The trained algorithms were validated using 255 liver WSIs to detect, classify, and quantify seven types of histopathological findings (including vacuolation, bile duct hyperplasia, and single-cell necrosis) in the liver. The algorithms showed consistently good performance in detecting abnormal areas. Approximately 75% of all specimens could be classified as true positive or true negative. In general, findings with clear boundaries with the surrounding normal structures, such as vacuolation and single-cell necrosis, were accurately detected with high statistical scores. The results of quantitative analyses and classification of the diagnosis based on the threshold values between “no findings” and “abnormal findings” correlated well with diagnoses made by professional pathologists. However, the scores for findings ambiguous boundaries, such as hepatocellular hypertrophy, were poor. These results suggest that deep learning-based algorithms can detect, classify, and quantify multiple findings simultaneously on rat liver WSIs. Thus, it can be a useful supportive tool for a histopathological evaluation, especially for primary screening in rat toxicity studies.