Artificial intelligence (AI), particularly deep learning algorithms, is gaining extensive attention for its excellent performance in liver disease-recognition tasks [1] [2] [3] [4]. Deep learning algorithms can automatically make a quantitative assessment of complex serum biomarkers results and achieve an increased accuracy for diagnosis with higher efficiency [1]. AI is widely used and getting increasingly popular in the medical diagnosing of the liver, including radiology, ultrasound, and nuclear medicine [5]. Further, AI can assist clinicians to make more accurate and reproductive liver disease diagnosis and also reduce the physicians’ workload.

Global prevalence of nonalcoholic fatty liver disease (NAFLD) is approximately 25% [6], including an estimated 46% of middle-aged Americans [7], establishing this as the most common chronic liver disease. It is expected that 20% - 30% of those with newly detected NAFLD will have already progressed to NASH; and among these, 10% - 20% will progress to cirrhosis and/or hepatocellular carcinoma [8] [9] [10] [11]. Not surprisingly, nonalcoholic steatohepatitis (NASH) is currently the second leading cause of liver disease among those awaiting liver transplantation in the United States [8]. NAFLD is considered a hepatic manifestation of metabolic syndrome components such as obesity, type 2 diabetes (T2DM), dyslipidemia, and insulin resistance. Among patients with T2DM, the prevalence of NAFLD may be as high as 70% and is often associated with cardiovascular disease [12]. Nonetheless, current practice guidelines fail to support routine screening for NAFLD/NASH in patients with T2DM due in part to the difficulty in properly diagnosing the disease in high-risk groups [7].

Percutaneous liver biopsy remains the gold standard for making a precise diagnosis of NAFLD with specification categorization and is necessary to assess the histopathologic criteria essential to making a diagnosis of NASH [13] [14]. Biopsy allows for confirmation of steatosis as well as determining the degree of lobular inflammation, ballooning, and fibrosis. Commonly used scoring systems for evaluating the severity of NAFLD include the NAFLD Activity Score and SAF score [15]. The FLIP (Fatty Liver Inhibition of Progression) Pathology Consortium has expanded the scoring system for steatosis (S), inflammation activity (A), and fibrosis (F) as the SAF Score, based on partially standardized visual features of microscopic pathology while separately assessing “steatosis”, “activity” (the sum of hepatocyte ballooning and lobular inflammation), and “fibrosis” through semi-quantitative ordinal scales [16]. Used in combination, these three criteria provide a more accurate, more comprehensive, and less subjective description for the diagnosis of NASH than the previously employed NAFLD Activity Score (NAS) scoring system [17]. The use of liver biopsies, however, carries limitations including pain, risk of complications from invasive procedure, inter-observer variation, and applicability of sampling location and tissue volume; which also introduce heterogeneity of the pathologic signs [18] [19] [20] [21].

The enormity of the NAFLD population and the complications associated with their disorders, raise concerns about employing frequent biopsy assessment of individuals, making its use impractical as a screening tool and/or for serial evaluations in metabolically compromised patients [22]. Moreover, numerous clinical practice guidelines including AASLD, EASL-EASD-EASO, APASL, and WHO recommend non-invasive biomarker-based diagnostic modalities to diagnose liver diseases [16] [23] [24] [25] [26].

Accordingly, non-invasive diagnostics have been developed to screen for and monitor liver disease. Because imaging technologies such as ultrasonography, magnetic resonance imaging (MRI), transient elastography (TE), and computed tomography (CT) are expensive, they are generally impractical for most serial evaluations [27]. Besides high cost, the operator dependence, lower sensitivity and range, radiation exposure, and limited availability are some of the limitations of imaging-based diagnosis of liver damage [28].

Considering those complications and limitations, clinicians have turned to serum-based biomarkers and their associated investigational or commercial algorithms for the presence of advanced fibrosis in NAFLD (e.g. NAFLD fibrosis score, FIB-4 index, aspartate aminotransferase [AST] to platelet ratio index [APRI]), serum biomarkers (Enhanced Liver Fibrosis [ELF] panel, Fibrometer, FibroTest, and Hepascore) [16] [23] [24] [25] [26] [29] [30]. Concurrently, nonalcoholic fatty liver disease (NAFLD) now represents the most common cause of abnormal liver blood tests and chronic liver disease in the Western world [31].

Models based on machine learning (ML) algorithms (computer aided diagnosis based on artificial intelligence) have been shown to classify liver disease into distinct categories with ~80% accuracy [32] [33]. Biomarker-based diagnostic methods have been proven to fulfil these requirements for diagnosis [34]. Different stages of steatosis, inflammation, and fibrosis produce characteristic molecular changes (biomarkers) which can be detected in the serum and provide a snapshot of the liver disease stage. Therefore, assessment via algorithmic derivations of the same three components of liver disease assessed by biopsy (steatosis, inflammation activity, and fibrosis) can lead to a provisional diagnosis of NAFLD or NASH.

Using the latest available artificial intelligence (AI) technology, we have improved second generation advanced algorithm LIVERFASt^TM to evaluate the diagnostic accuracy of ML biomarker algorithms to assess liver damage. Applying the diagnostic modality of FLIP pathology scoring system, the ML algorithm-based LIVERFASt^TM produced a modified SAF score to evaluate hepatic steatosis, necro-inflammatory activity, and fibrosis.

The updated algorithm has been trained to accommodate new data using AI, adding flexibility and improving from notoriously outdated linear regression models. The new ML algorithm improves accuracy of prediction of SAF score.

In this work there are three different networks, one for each lesion: fibrosis; inflammation activity; and steatosis. The three NNs shared common settings. They each used rectified linear unit as the activation function, mean absolute error as the loss function, Glorot uniform as the weight initialization methods, and Adam as the optimization technique. Other settings were determined through a cross-validation grid search (number of hidden layers, number of neurons in each layer, batch size, and Adam parameters).

The aim of this report is two-fold. First, to show utilization of ML to replicate the diagnostic accuracy of LIVERFASt^TM’s latest generation of AI for algorithm using anthropometric and biomarker parameters to determine hepatic steatosis, necro-inflammatory activity, and fibrosis. Second, to show how the use of the existing SAF scoring system and its interpretation guided the creation of a new algorithmic tool, ML-based, providing accurate prediction of the SAF score determinations of NAFLD/NASH with a cost-efficient approach. Here, we have studied real-world data collected from a large number of patients whose blood samples were analysed using ML-based algorithm for the evaluation of fatty liver disease. Our results validate the applicability and accuracy of LIVERFASt^TM in a Southeast Asian population.

A multi-country clinical dataset of 13,068 individuals over a period of 3 years is incorporated in this study. The degree of distribution of each country and demographics figures are given in Table 1, as well as gender specifics and average BMI.

For the evaluation of liver disease, fibrosis, inflammation activity and steatosis in this extended clinical dataset, a ML LIVERFASt^TM algorithm tool (see Figure 1) was applied based in the features depicted in Table 2, a2M, ApoA1, haptoglobin, total bilirubin, GGT, AST, ALT, total cholesterol, triglycerides and fasting glucose, as well as gender, age and BMI. For further evaluation of the AI-based algorithm, a database consisting of 2862 unique medical assessments of biomarkers and biopsy reports were trained and validated.

The training dataset (n = 1027) consisted of 60% males, while the validation dataset (n = 1835) had a greater proportion of males (77%). The training set was also slightly older (~51 years to 45 years old), and with a slightly lower mean BMI. The medical records contained liver staging scores generated by the AP-HP set of algorithms [37]. These were used as the benchmark against which the neural network outputs were evaluated. Per the histograms in Figure 3, most of the patients included in the training set were in early stages of fibrosis and inflammatory activity at the time of assessment. Among the patients with steatosis, the repartition of scores is more balanced throughout the full scoring range. Training and validation datasets displayed roughly similar but not identical distributions in terms of features as well as in terms of outcome in scoring test.

NASH is diagnosed based on an overall assessment by a pathologist using scoring systems such as the steatosis, activity, and fibrosis (SAF) score, which evaluates of the presence and extent of each individual component of steatosis, inflammation, and ballooning. NASH can be diagnosed using a validated algorithm based on the SAF scoring system [17]. The correlation between the estimated SAF score and the NAFLD/NASH diagnosis that LIVERFASt^TM provides is shown in Table 3.

As seen in Table 4, the MAE function evaluated the machine learning performance of the NNs the using machine learning as well as the MaxAE of the AI-based algorithm test compared to benchmark comparative tests. For the three NNs of the LIVERFASt^TM ML algorithm, the order of magnitude of the MAE was 1E−3, compared to benchmark algorithm that obtained a precision of 1E−2 [37]. Aside from this, cross-validated grid search was used to optimize the hyperparameters which played a role in the performance of the resulting neural network. The R² values for all three networks are very close to 1, with 0.99992 for fibrosis, 0.99952 for activity and 0.99991 for steatosis which shows a very high level of linear correlation between the scores generated by the neural networks and those generated by the benchmark comparative tests.

As discussed previously, NAFLD may progress to NASH and subsequently cirrhosis and HCC. In addition, NASH is associated with increased mortality compared with the general population [38]. If diagnosed early, weight loss and lifestyle modification may improve liver histology and prevent further damage [38]. Thus, it is essential to diagnose NAFLD and NASH to prevent progression to cirrhosis or HCC. Biopsy examination has been the gold standard for evaluation of liver health and disease diagnosis and remains the primary standard to which all other methods for liver evaluation are compared (e.g. multiple imaging techniques; serology).

As the prevalence of NAFLD increases worldwide, a critical need has arisen for reliable tools that are non-invasive, safe, quick, inexpensive, and suitable to evaluate patients with metabolic complications or provide sensible review of treatment progress for individual patients or intervention trials [39]. Liver biopsy, though compelling, carries and element of risk and is subject to varying interpretations and inter-observer discordance [20]. Due to the limitations of these conventional methods, the necessity for a non-invasive, highly sensitive, specific, easy and readily available and cost-effective method to diagnose fatty liver disease is warranted [25].

In this study, we analysed the real-world data of biomarker-based diagnosis of non-alcoholic fatty liver disease in 13,068 subjects from Southeast Asia. The evaluation was made by AI biomarker-based algorithm LIVERFASt^TM which was developed using ML second generation logarithms.

We have taken quantitative scores defined by the FLIP consortium where NASH is diagnosed based on an overall assessment by a pathologist using validated algorithm scoring systems such as the steatosis, activity, and fibrosis (SAF) score [17]. The correlation between the estimated SAF score and the NAFLD/ NASH diagnosis that LIVERFASt^TM is using is shown in Table 3. Approximately 13.41% of the patient population was diagnosed with NAFLD or NASH.

Determination of disease severity is a challenging element of the diagnostic workup of patients with NAFLD. The goal here is to identify patients with more advanced disease at increased risk for advancing further to irreversible disease with associated morbidity and mortality. From the population analysed, 1.08% had confirmed NASH (Sx > 0, Ay > 2, Fz = 1 - 2) and 1.49% had advanced NASH.

A challenge with NNs is that they have a very high number of hyperparameters to tune. Therefore, finding the optimal set of parameters for a given problem can be long and cumbersome. In this work there are three different problems, one for each lesion: fibrosis; inflammation activity; and steatosis. The three NNs shared common settings. They each used rectified linear unit as the activation function, mean absolute error as the loss function, Glorot uniform as the weight initialization methods, and Adam as the optimization technique. Other settings were determined through a cross-validation grid search with number of hidden layers, number of neurons in each layer, batch size, and Adam parameters. Here, the tool used for this process was Keras, The Python Deep Learning library [40].

Standard metrics used to describe the accuracy of regression models have been computed to assess the performance of the new models. MAE, MaxAE and coefficient of determination (R²) are computed over validation sets of anthropometric and laboratory assessments that were not used during the training phase. The values (Table 4) for these three metrics are evidence of the ability of the new models to generalize readily to unknown data, thereby validating their high predictive power over the most significant range of medical relevance. Having developed the tools to reach this milestone, it is now possible for researchers to provide significant improvements to create the second-generation assessment tools that the clinical community seeks for even more useful non-invasive liver diagnostics. One limitation of the study is the discrepancies in gender, age and to a lesser extent, BMI between the training data set of medical assessments and the validation dataset. The algorithm is being upgraded as more patient medical assessments are applied to the current model, additional clinical studies are in progress to that end.

Furthermore, replacing or adding biomarkers to those described here may provide solutions that improve upon the first-generation accuracy. With the use of advanced ML techniques, investigators may also target the removal of specific, less significant biomarkers used in the first generation. Thus, while improving sensitivity and specificity, the new diagnostic tools might also increase cost-effectiveness (assuming equal or better accuracy and/or the cumulated expense of deleted biomarkers is greater than the expense of any new biomarkers). A thorough analysis for the selection of innovative and more accurate biometrics would be an obvious first step for such work. Ideally the pursuit of multiple putative modifications would include the realization of additional clinical studies which would directly provide pathology assessments from high quality biopsy tissue and an abundance of potential biomarkers.

The most immediate utilization of the algorithmic assessments described here is the application of these tools by any licensed physician or health care professional. At the same time, the primary focus and ultimate goal of this work is the development of advanced algorithms that will more accurately diagnose whole liver pathology; predict the clinical path of individuals at all stages of the NAFLD spectrum; and provide an efficient and improved system with which to examine new and critically needed therapeutic interventions that mitigate or reverse NAFLD progression.

The modified SAF scoring system generated by LIVERFASt^TM provided a simple and convenient diagnosis of NAFLD and NASH and staging of the three liver lesions as shown in a cohort of South East Asia.

The use of noninvasive liver tests in extended populations provides an accurate diagnosis of liver pathology, prediction of clinical path of individuals at all stages of liver diseases, and an efficient system for therapeutic interventions. Non-invasive diagnostic tools such as LIVERFASt^TM are easy to perform, less expensive, and readily available and aid to the early diagnosis and better prognosis in patients with NAFLD and NASH.

In accord with the 2016 EASL-EASO-EADO Guidelines, use of noninvasive serum biomarkers should aim to: 1) in primary care settings, identify the risk of NAFLD among individuals with increased metabolic risk; 2) in secondary and tertiary care settings, identify those with worse prognosis, e.g. severe NASH; 3) monitor disease progression; 4) predict response to therapeutic interventions. Achieving these objectives could reduce the need for liver biopsy.

The applicability of LIVERFASt^TM extends beyond hepatologists and includes primary care providers, as well as endocrinologists, diabetologists and other medical disciplines that manage and monitor fatty liver, liver fibrosis and liver activity. LIVERFASt^TM test has a potential role worldwide in the clinical care settings as screening for NAFLD and NASH population at risk.

The authors wish to thank Imtiaz Alam, MD that provided significant background on the liver disorders addressed by this algorithmic and contributed to revision of the final version of the manuscript. Nelly Conus, PhD contributed to revision of the final version of the manuscript. Hawley K. Linke, PhD provided inputs for important intellectual content for results interpretation and contributed to revision of the final version of the manuscript.

The authors declare no conflicts of interest regarding the publication of this paper.

Aravind, A., Bahirvani, A.G., Quiambao, R. and Gonzalo, T. (2020) Machine Learning Technology for Evaluation of Liver Fibrosis, Inflammation Activity and Steatosis (LIVERFASt^™). Journal of Intelligent Learning Systems and Applications, 12, 31-49. https://doi.org/10.4236/jilsa.2020.122003