Evaluation methods of hepatic steatosis: From conventional techniques to emerging biomarkers

Abstract

Accurate assessment of hepatic steatosis is essential for diagnosing, staging, and monitoring metabolic dysfunction-associated steatotic liver disease. This review comprehensively overviews conventional and emerging hepatic steatosis evaluation methods. Noninvasive imaging techniques such as ultrasound, controlled attenuation parameter, and magnetic resonance imaging-derived proton density fat fraction are discussed alongside invasive reference standards such as liver biopsy. The review also highlights the role of blood-based biomarkers, including fibroblast growth factor 21, cytokeratin-18, type III procollagen peptide, and Mac-2 binding protein glycosylation isomer, as well as novel approaches such as epigenetic markers, artificial intelligence–assisted imaging, and digital pathology. Each method is presented with consideration of its diagnostic performance, clinical utility, and limitations. By integrating these modalities into multimodal assessment strategies and incorporating dynamic endpoints such as magnetic resonance imaging-derived proton density fat fraction (known as magnetic resonance imaging-derived proton density fat fraction)-based fat reduction as a therapeutic response marker, clinicians can improve diagnostic accuracy, risk stratification, and therapeutic guidance.

Key Words: Metabolic dysfunction-associated steatotic liver disease; Hepatic steatosis; Magnetic resonance imaging-derived proton density fat fraction; Controlled attenuation parameter; Blood-based biomarkers; Artificial intelligence; DNA methylation

Core Tip: This review provides a comprehensive overview of hepatic steatosis evaluation methods, ranging from conventional ultrasound and liver biopsy to advanced techniques such as magnetic resonance imaging-derived proton density fat fraction, attenuation-based ultrasound, and emerging blood-based and epigenetic biomarkers. By integrating multimodal diagnostic strategies, including artificial intelligence-assisted imaging and methylation profiling, clinicians can improve the diagnosis, risk stratification, and therapeutic monitoring of metabolic dysfunction-associated steatotic liver disease. The review also highlights the evolving role of steatosis quantification as a treatment response marker and future directions in precision hepatology.

INTRODUCTION

Hepatic steatosis, characterized by excessive fat accumulation in the liver, has become increasingly prevalent worldwide, affecting approximately 25% of the global population. However, the prevalence varies by region with the highest rates observed in South America and the Middle East (up to 30%-35%) followed by North America and parts of Asia. At the same time, Africa shows a comparatively lower prevalence[1]. Metabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as non-alcoholic fatty liver disease (NAFLD), represents the predominant cause of chronic liver disease, significantly raising the risk of progression to severe conditions such as metabolic dysfunction-associated steatohepatitis (MASH), cirrhosis, hepatocellular carcinoma (HCC), and cardiovascular diseases[2]. Therefore, accurate assessment of hepatic steatosis is crucial for early detection, clinical decision-making, and monitoring treatment efficacy[3].

Traditionally, liver biopsy has been regarded as the reference standard for assessing hepatic steatosis severity; however, it is invasive, costly, and prone to sampling variability[4]. Conventional ultrasound, though widely accessible and noninvasive, relies on qualitative assessment and lacks reproducibility and quantification capability[5]. Recent advancements, including magnetic resonance imaging-derived proton density fat fraction (MRI-PDFF) and elastography techniques such as transient elastography and shear wave elastography, offer reliable, noninvasive alternatives for quantitative assessment[6]. Furthermore, emerging biomarkers, including fibroblast growth factor 21 (FGF21), cytokeratin-18 (CK-18), type III procollagen peptide (Pro-C3), Mac-2 binding protein glycosylation isomer (M2BPGi), and epigenetic markers such as DNA methylation, are receiving increasing attention due to their potential to enhance diagnostic accuracy and prognostic capability[7,8].

The terminology for NAFLD has recently been redefined as MASLD. While both terms describe hepatic steatosis not attributable to significant alcohol intake or other specific liver diseases, the key distinction lies in the diagnostic criteria. NAFLD was defined by the presence of hepatic steatosis after excluding secondary causes. In contrast, MASLD requires evidence of hepatic steatosis in combination with at least one cardiometabolic risk factor such as obesity, type 2 diabetes, dyslipidemia, or hypertension[9]. This redefinition emphasizes the metabolic underpinnings of the disease and aims to improve risk stratification and clinical management. This review provides a comprehensive overview of current methodologies, including conventional imaging, advanced modalities, blood-based biomarkers, and emerging experimental techniques, to inform clinical management and guide future research.

MECHANISMS OF HEPATIC STEATOSIS IN MASLD

MASLD develops through multiple overlapping mechanisms that promote hepatic triglyceride accumulation. Insulin resistance plays a central role, increasing adipose tissue lipolysis and flux of free fatty acids into the liver. At the same time, hyperinsulinemia and hyperglycemia enhance de novo lipogenesis via upregulation of transcription factors such as SREBP-1c and carbohydrate-responsive element-binding protein, further augmenting lipid synthesis[8]. Mitochondrial dysfunction and oxidative stress reduce fatty acid β-oxidation, while endoplasmic reticulum stress contributes to impaired lipid disposal and very low-density lipoprotein secretion. Emerging evidence highlights that defective selective autophagy, particularly lipophagy, impairs the degradation of lipid droplets and aggravates steatosis[10]. Collectively, these processes converge to create a “lipid overload” state, in which both increased input and reduced disposal of fatty acids drive hepatic steatosis (Figure 1).

Figure 1 Mechanisms underlying hepatic steatosis in metabolic dysfunction-associated steatotic liver disease. Hepatic steatosis develops when fatty acid input exceeds disposal. Major contributors include insulin resistance, mitochondrial dysfunction, oxidative stress, and endoplasmic reticulum stress. In addition, defective selective autophagy (lipophagy) has been identified as an emerging mechanism that limits lipid droplet degradation and may exacerbate steatosis. DNL: De novo lipogenesis; FFA: Free fatty acid; TG: Triglyceride; VLDL: Very low-density lipoprotein.

NONINVASIVE METHODS FOR HEPATIC STEATOSIS EVALUATION

Definition and diagnostic criteria of hepatic steatosis

The histological definition of hepatic steatosis is the presence of macrovesicular fat droplets in more than 5% of hepatocytes as adopted by international guidelines including the American Association for the Study of Liver Diseases, the European Association for the Study of the Liver, and the Japan Society of Hepatology[11,12]. This threshold is based on liver biopsy and is a reference standard for evaluating imaging modalities. However, in conventional B-mode ultrasound, which relies on echogenicity-based features, sensitivity and specificity are significantly reduced when hepatic fat content is below 30%, and the method may fail to detect microvesicular steatosis[12,13]. Recently, ultrasound attenuation-based methods have shown promise in detecting lower degrees of steatosis, such as ≥ 5%[14-16].

Conventional ultrasound

Conventional ultrasound imaging utilizes high-frequency sound waves from a transducer that penetrates tissues and reflects at interfaces of differing acoustic impedance. The ultrasound probe emits sound waves that interact with tissues, generating echoes. These echoes return to the transducer, creating images based on acoustic impedance differences. Hepatic steatosis alters the acoustic properties of the liver, increasing its echogenicity[17].

Key sonographic features indicating hepatic steatosis

Liver-kidney contrast: The liver appears brighter (more echogenic) than the renal cortex.

Liver-spleen contrast: Increased hepatic echogenicity compared with the spleen.

Posterior beam attenuation: Reduction in the intensity of the ultrasound signal beyond the fatty liver, impairing visualization of deeper liver structures and the diaphragm.

Reduced visibility of intrahepatic vascular structures: Difficulty delineating intrahepatic vessels due to the diffusely increased echogenicity[18,19]. In clinical practice, a diagnosis of hepatic steatosis typically requires at least two of these sonographic features to improve diagnostic specificity. Trained sonographers or radiologists should perform the evaluation ideally, as interpretation can be subjective and inter-observer variability is a known limitation of conventional ultrasound[18,19].

The severity of hepatic steatosis is commonly graded as follows: Grade 1 (mild), with slightly increased echogenicity with well-preserved visualization of the diaphragm and intrahepatic vessels; grade 2 (moderate), with moderately increased echogenicity with partially obscured diaphragm and intrahepatic vessels; grade 3 (severe), with markedly increased echogenicity and severely impaired visualization or complete obscuration of the diaphragm and intrahepatic vessels, accompanied by significant posterior attenuation[18].

Although some practitioners use semi-quantitative grading systems to classify steatosis severity by ultrasound (e.g., grade 1-3), such classifications are not standardized across guidelines or widely validated in clinical trials. Their interpretation remains subjective and dependent on operator experience[18].

Clinical utility and limitations of conventional ultrasound

B-mode ultrasonography remains widely used worldwide as a first-line screening tool due to its noninvasiveness, low cost, and accessibility. While conventional B-mode ultrasound is commonly employed in general practice and population-based screenings, academic and specialized centers increasingly adopt quantitative ultrasound techniques such as attenuation imaging (ATI)[20], ultrasound-guided attenuation parameter (UGAP)[21], and MRI-PDFF[6]. In Western countries, including the United States and Europe, MRI-PDFF[6] and controlled attenuation parameter (CAP)[22] are more frequently utilized, particularly in specialized settings. Nonetheless, B-mode ultrasound plays an essential role in the global clinical workflow for the initial detection of hepatic steatosis. In addition, a Japan-originated technology, the attenuation technique (ATT) implemented in Aplio ultrasound systems by Canon Medical Systems Corp. has shown promise for quantitatively assessing hepatic steatosis. Though less widely adopted globally than CAP or ATI, ATT has significantly correlated with histologically confirmed fat accumulation, underscoring its potential clinical value[23].

It is now recognized that hepatic fibrosis rather than the degree of steatosis or whether the patient has progressed from simple steatosis to MASH plays a more significant role in determining prognosis. However, identification of steatosis remains a necessary preliminary step in clinical assessment. Accordingly, this review emphasized the increasing clinical value of attenuation-based ultrasound methods while acknowledging the continued role of conventional B-mode ultrasound as a supportive tool. Focal fatty infiltration requiring differentiation from hepatic tumors is beyond the scope of this review. Owing to its accessibility, safety, and cost-effectiveness, B-mode ultrasound continues to serve as the gateway for identifying individuals with potential hepatic steatosis in clinical and population-based settings.

Attenuation-based ultrasound techniques

Ultrasound waves attenuate exponentially as they propagate through biological tissues due to scattering and absorption with absorption being the dominant factor in beam-shaped transmissions. The degree of attenuation is represented by the attenuation coefficient α, which is frequency-dependent and described by the equation α = afⁿ (dB/cm) where ‘f’ is frequency, and ‘n’ is typically close to 1 in soft tissues. The proportionality constant ‘a’ (in dB/MHz/cm) varies by tissue type and pathological state. Fatty liver exhibits higher attenuation than normal liver, allowing for quantitative assessment using ultrasound[17].

CAP: Developed by Echosens in France, CAP is integrated into FibroScan devices and measures ultrasound attenuation at 3.5 MHz using a transient elastography probe. This allows simultaneous assessment of liver stiffness and steatosis[22,24-26].

ATI: Developed by Canon Medical Systems in Japan, ATI generates real-time attenuation coefficient maps by analyzing the reduction of ultrasound signal intensity during B-mode imaging. This technique enables visual and quantitative assessment of hepatic steatosis and has been validated against MRI-PDFF, showing strong correlation (r ≈ 0.81) and excellent diagnostic performance with area under the receiver operating characteristic curve (AUROC) values up to 0.91[20].

UGAP: Developed by GE Healthcare Japan, UGAP calculates the attenuation coefficient in real time during routine B-mode imaging by placing a region of interest in the liver parenchyma. This method enables a simple, reproducible, and quantitative assessment of hepatic steatosis and has demonstrated a strong correlation with MRI-PDFF in clinical studies[21,27].

ATT: Developed by Canon Medical Systems Corp. (formerly Hitachi), ATT calculates the ultrasound attenuation coefficient (dB/cm/MHz) based on the reduction in signal amplitude during B-mode imaging. This method significantly correlates with histologically confirmed hepatic steatosis and enables quantitative assessment in routine clinical practice[23].

These technologies provide numerical values reflecting hepatic fat content and have been validated against liver biopsy or MRI-PDFF. Representative studies have demonstrated the following diagnostic performance for detecting ≥ 5% steatosis: CAP, with AUROC 0.77-0.95, cutoff 233-288 dB/m[22,24-26]; ATI, with AUROC 0.85, cutoff 0.66 dB/cm/MHz[27]; UGAP, with AUROC 0.90-0.92, cutoff 0.53-0.60 dB/cm/MHz[20,21,28]; and ATT, with AUROC 0.79, cutoff 0.62 dB/cm/MHz[23].

Correlation coefficients between attenuation-based ultrasound values and reference standards such as MRI-PDFF or histological steatosis typically range from r = 0.50-0.81 as demonstrated in validation studies of UGAP[20], ATI[21], and ATT[23]. These techniques allow for the reliable detection of mild hepatic steatosis (5% fat) and are especially valuable for early intervention and population screening, where conventional B-mode ultrasound often lacks sensitivity.

While hepatic fat is the primary determinant of attenuation, liver fibrosis may also influence measurements. However, some studies have shown no significant interaction between fat and fibrosis[20,29]. Thus, attenuation-based methods serve as powerful tools for early, noninvasive assessment of steatosis and are increasingly recommended in clinical practice.

CAP is currently the most widely used attenuation-based technique worldwide due to its integration into FibroScan devices, which are extensively adopted in clinical practice for the simultaneous assessment of liver stiffness and steatosis. Its global deployment, ease of use, standardized protocols, and inclusion in international clinical guidelines have contributed to more validation studies than newer technologies such as UGAP or ATI. While UGAP has shown higher AUROC values in certain studies, further research and broader clinical implementation are required to establish its superiority over CAP in routine practice.

MRI-based techniques

MRI-PDFF is the most accurate noninvasive method for quantifying hepatic fat content. Unlike conventional MRI techniques for anatomical imaging or qualitative fat suppression, MRI-PDFF employs advanced chemical shift-encoded imaging sequences to separate signals from water and fat protons precisely. This enables quantification of liver fat as a percentage that is independent of scanner manufacturer or magnetic field strength (typically 1.5 Tesla or 3.0 Tesla). MRI-PDFF is designed to yield consistent fat quantification across scanners when appropriate calibration and standardized imaging protocols are applied.

The MRI-PDFF sequence typically uses multiecho gradient-recalled echo imaging to acquire signal data at different times. These data calculate the fat fraction across the liver on a voxel-by-voxel basis. Modern MRI systems from major vendors (e.g., GE Healthcare, Philips, Siemens Healthineers) can perform PDFF imaging if appropriately configured. Still, software licensing and protocol optimization are often required, limiting accessibility in general hospitals.

MRI-PDFF has demonstrated high diagnostic accuracy, with AUROC values ranging from 0.90 to 0.98 for detecting ≥ 5% steatosis. AUROC is a statistical measure that reflects a test's diagnostic performance by plotting sensitivity against 1-specificity. An AUROC of 1.0 indicates perfect discrimination, whereas a value of 0.5 suggests no diagnostic capability. It correlates strongly with histologic steatosis grade (r > 0.90) and is widely used as a reference standard in clinical trials of MASLD therapies[30-32].

Advantages of MRI-PDFF include its ability to quantify hepatic fat content across the entire liver, thereby minimizing sampling bias, and its high reproducibility, which makes it suitable for monitoring longitudinal changes in hepatic steatosis. Additionally, MRI-PDFF can simultaneously assess other liver characteristics, such as iron content, during the same examination.

However, MRI-PDFF also has several limitations. It is more expensive than ultrasound or biochemical tests and may not be readily available in general medical facilities or community hospitals that lack specialized MRI protocols. The procedure requires longer scan times and breath-holding, which can be burdensome for some patients, particularly those with limited respiratory function. Moreover, MRI is contraindicated in patients with specific implanted medical devices or severe claustrophobia. Despite these limitations, MRI-PDFF is increasingly adopted in tertiary centers and research settings to validate newer diagnostic modalities such as ultrasound-based ATI and serum biomarkers.

Artificial intelligence-enhanced imaging

Artificial intelligence (AI) has shown great promise in improving imaging-based evaluation of MASLD. Conventional ultrasound, computed tomography (CT), and magnetic resonance imaging (MRI) often suffer from operator dependence, limited sensitivity for mild steatosis, and variable reproducibility. Deep learning algorithms applied to standard-of-care ultrasound images have demonstrated high accuracy in detecting and grading steatosis, with area under the curve values approaching 0.95 in differentiating mild from moderate-to-severe disease[33]. These models can reduce inter-observer variability and provide automated quantification of hepatic fat, even in subtle or heterogeneous cases.

AI has also been applied to CT and MRI, where convolutional neural networks enable automated segmentation and fat fraction quantification, enhancing both efficiency and reproducibility in clinical and research settings[34]. Broader reviews of radiomics and AI applications in medical imaging highlight the potential of deep learning to integrate imaging-derived features for precision diagnostics[35,36]. Importantly, AI-based longitudinal analysis of imaging data allows sensitive detection of dynamic changes in hepatic fat content, supporting its use as a non-invasive tool for monitoring therapeutic response. Integrating imaging-derived features with clinical and biochemical data may improve risk stratification, positioning AI-assisted imaging as a versatile adjunct in precision hepatology.

Blood-based biomarkers

In addition to imaging techniques, blood-based biomarkers are increasingly used to assess hepatic steatosis, particularly when imaging modalities are unavailable or prohibitively expensive. This section highlights key characteristics, diagnostic utility, and limitations of blood-based biomarkers.

Liver enzymes

Aspartate aminotransferase (AST) and alanine aminotransferase (ALT) are commonly measured liver enzymes. ALT is primarily localized in the cytoplasm of hepatocytes and is released into the circulation with hepatocellular injury, particularly in MASLD, where steatosis and metabolic stress cause mild-to-moderate ALT elevations. In contrast, AST exists both in the cytoplasm and mitochondria of hepatocytes, and its levels tend to rise in the setting of more severe liver injury, including fibrosis or alcoholic liver disease, where mitochondrial damage is prominent[37]. The AST/ALT ratio is used to distinguish between different types and stages of liver disease: A ratio > 1 may suggest advanced fibrosis or alcoholic liver disease whereas a ratio < 1 is typical of early-stage MASLD[38]. However, these enzymes lack sensitivity and specificity for early steatosis, and levels can be normal even in biopsy-proven disease[39]. A comparative study further demonstrated that the AST/ALT ratio was significantly higher in patients with alcoholic liver disease[40].

Although aminotransferases lack sufficient sensitivity and specificity to serve as standalone diagnostic markers, their accessibility and low cost make them useful for initial screening. Beyond diagnosis, serial measurement provides a practical means of monitoring therapeutic response. Clinical trials have shown that lifestyle interventions or pharmacological treatments such as pioglitazone or vitamin E often reduce ALT levels in parallel with histological improvement[41]. However, ALT normalization does not necessarily exclude the presence of steatohepatitis or fibrosis, as highlighted in subsequent studies[42,43]. Thus, ALT and AST should be interpreted within a multimodal assessment framework, complementing findings from imaging modalities and non-invasive fibrosis marker testing rather than in isolation as definitive diagnostic tools.

CK-18

CK-18 is an intermediate filament protein localized in the cytoplasm of hepatocytes and other epithelial cells. During apoptosis, which is a hallmark of nonalcoholic steatohepatitis (NASH) but not of simple steatosis, CK-18 is cleaved specifically by caspases such as caspase-3. The resulting fragments are released into the bloodstream and can be detected using ELISA assays that employ antibodies like M30, which recognize the cleaved form of CK-18[44,45].

CK-18 is considered a valuable biomarker for NASH because it is highly expressed in hepatocytes, specifically cleaved during apoptosis, and remains stable and detectable in serum. These features align with the pathophysiological processes of NASH, making CK-18 a useful noninvasive marker for distinguishing NASH from simple steatosis and evaluating disease severity[45,46]. However, its performance varies across populations[44] with reported sensitivities ranging from 53% to 78% and specificities from 59% to 95% across different clinical centers. These variations may be attributed to differences in patient characteristics, disease severity, and possibly genetic or ethnic backgrounds. Despite this variability, CK-18 remains one of the most extensively studied and promising noninvasive biomarkers for identifying NASH.

FGF21

FGF21 is an endocrine hormone primarily produced in the liver and secreted into the bloodstream. While the liver is the primary source of circulating FGF21, lower expression levels are observed in white adipose tissue, skeletal muscle, and the pancreas[47]. Hepatic production of FGF21 is upregulated in response to metabolic stress, such as fasting, mitochondrial dysfunction, and lipid overload[47,48].

FGF21 exerts systemic effects by targeting distant organs. In white adipose tissue, it enhances lipolysis, promotes fatty acid oxidation, and increases insulin sensitivity partly through the induction of adiponectin, mediated via the FGFR1c receptor in conjunction with the β-klotho co-receptor[49]. In the central nervous system, particularly the hypothalamus, it modulates energy expenditure and appetite regulation[50]. It also exerts protective effects on pancreatic β cells and may influence glucose metabolism in skeletal muscle through adipokine-mediated signaling[47,49]. Although FGF21 levels are elevated in obesity and MASLD as a compensatory response to metabolic stress, many individuals exhibit a blunted physiological response to FGF21, a phenomenon known as FGF21 resistance. This resistance resembles insulin resistance, in which hormone levels are elevated but target tissues fail to respond adequately, limiting the beneficial metabolic effects of the hormone. In MASLD, this phenomenon may reduce the effectiveness of FGF21 in promoting hepatic lipid clearance and improving insulin sensitivity, thereby contributing to the persistence or progression of hepatic steatosis[48].

During fasting, activation of the nuclear receptor peroxisome proliferator-activated receptor alpha (PPARα) induces hepatic FGF21 transcription, enhancing fatty acid oxidation and ketogenesis as adaptive mechanisms to reduce hepatic lipid burden[51]. Moreover, nutrient deprivation activates autophagy, possibly contributing to FGF21 induction and linking intracellular quality control with endocrine responses. While lipophagy facilitates lipid clearance, autophagy deficiency paradoxically induces FGF21 as a mitokine, potentiating FGF21-mediated protection against steatosis[52].

Pro-C3

Pro-C3 is a circulating biomarker that reflects the formation of type III collagen, a major component of the extracellular matrix deposited during fibrogenesis. It is generated when type III procollagen is cleaved by specific enzymes during collagen maturation, releasing the N-terminal pro-peptide (Pro-C3) into the bloodstream[53]. Although Pro-C3 is not liver-specific and can be elevated in other fibrotic conditions, in MASLD hepatocyte injury and inflammation activate hepatic stellate cells, the principal producers of collagen in the liver. Activated HSCs upregulate type III collagen synthesis, increasing serum Pro-C3 levels. Therefore, elevated Pro-C3 is a marker of active hepatic fibrogenesis[53]. It has demonstrated utility in identifying patients with advanced fibrosis and is a key component of the ADAPT score, which combines age, diabetes status, and Pro-C3 levels to estimate the probability of advanced fibrosis[54].

M2BPGi

M2BPGi is a glycosylation isomer of Mac-2 binding protein secreted by hepatic stellate cells. In liver fibrosis, particularly during extracellular matrix remodeling, hepatic stellate cells activate and modify glycosylation patterns of secreted proteins, including Mac-2 binding protein. These glycosylation changes involve increased fucosylation and branching of sugar chains on Mac-2 binding protein, which can be detected by specialized assays using lectins (proteins that bind specific carbohydrate structures). In the M2BPGi assay, a particular lectin called Wisteria floribunda agglutinin detects these unique glycosylation patterns. Wisteria floribunda agglutinin selectively binds to terminal N-acetylgalactosamine residues that become more prominent on Mac-2 binding protein as liver fibrosis progresses[55]. This specificity allows the assay to distinguish glycosylation-altered forms of the protein, making M2BPGi a reliable surrogate marker of fibrogenic activity. These alterations reflect ongoing fibrotic activity and disease progression[56].

In MASLD, progression to fibrosis is characterized by activation of stellate cells and increased secretion of glycosylated proteins, leading to elevated M2BPGi levels. Although M2BPGi is not exclusively liver-specific, its serum concentration correlates with the severity of liver fibrosis. Lectin-based assays, such as those employing Wisteria floribunda agglutinin, have been validated to detect these changes effectively[56,57]. Multiple clinical studies have demonstrated its diagnostic and prognostic utility in MASLD and other chronic liver diseases[57,58].

Despite its clinical promise, M2BPGi has seen limited adoption in Western countries due to several factors. It was developed and validated primarily in Japan, relies on specialized lectin-based assay systems not widely available outside Asia, and lacks regulatory approval, such as a Food and Drug Administration or CE mark required for widespread clinical use. Furthermore, Western clinical guidelines have prioritized alternative biomarkers like the fibrosis-4 (FIB-4) index[59], enhanced liver fibrosis score[60], and Pro-C3 with broader international validation and infrastructure support. These factors contribute to the regional variation in the clinical use of M2BPGi.

AI applications in blood biomarker analysis

AI has also been applied to blood biomarker analysis in MASLD. To develop non-invasive diagnostic models, machine learning algorithms can integrate conventional laboratory parameters, metabolic indices, and omics data. In proof-of-concept studies, supervised learning approaches using serum-based omics have demonstrated the ability to identify patients with steatohepatitis and advanced fibrosis[61]. More recently, longitudinal analyses of population-based cohorts have employed machine learning to combine biochemical and clinical indicators for risk stratification, including prediction of all-cause mortality in MASLD[62]. These applications highlight the potential of AI-assisted biomarker models to provide accessible, reproducible, and noninvasive tools for disease detection and prognosis.

Animal models for developing non-invasive diagnostic approaches

Animal models of MASLD provide a valuable platform for discovering and validating novel noninvasive diagnostic and monitoring tools. Both dietary and genetically induced models replicate key features of human disease and allow systematic evaluation of candidate biomarkers such as circulating metabolites, cytokines, and apoptosis markers[63,64]. In parallel, proteomic approaches have identified serum biomarkers associated with fibrosis in patients with NASH, providing potential translational targets for validation in preclinical animal models[65].

Limitations and clinical utility

Although blood biomarkers offer a noninvasive alternative for assessing liver disease, their selection and interpretation should be based on the clinical context.

ALT and AST are inexpensive and widely available, but their interpretation requires caution. They may remain normal in early MASLD[39] and are influenced by factors such as alcohol intake or muscle injury[37]. Mild elevations, particularly in ALT, may prompt further investigation with imaging. An AST/ALT ratio > 1 can suggest advanced fibrosis[38]. CK-18 may be applied to screen for MASH in specialized settings or research, especially where biopsy is not feasible. Elevated levels suggest hepatocyte apoptosis but require standardized assays for reliable interpretation[44].

FGF21 could be informative in identifying patients with metabolic dysregulation contributing to MASLD, but its clinical role remains investigational due to 'FGF21 resistance'[48]. Pro-C3 is a promising marker for fibrogenesis and may be utilized in risk stratification for advanced fibrosis or in combination with other scores (e.g., ADAPT score), particularly in specialized centers[53]. M2BPGi has been validated in Japanese populations and may support fibrosis assessment where the assay is available; however, its utility remains regionally restricted and should not replace globally endorsed markers[58].

Several studies have proposed multimarker panels for the noninvasive diagnosis of MASH. For example, Younossi et al[66] developed and validated a biomarker panel predictive of histologic resolution of MASH. Meanwhile, Shen et al[67] demonstrated that combinations of serum biomarkers can accurately identify MASH without the need for biopsy. In clinical practice, these biomarkers should not be used in isolation. Instead, they serve best as components of multimodal assessment strategies that combine imaging (e.g., ultrasound, MRI-PDFF) and clinical parameters to improve diagnostic accuracy, stratify fibrosis risk, and guide monitoring during treatment.

Epigenetic modifications and DNA methylation

Recent advances in molecular biology have highlighted the role of epigenetic mechanisms, particularly DNA methylation, in the pathogenesis and progression of hepatic steatosis and related MASLD. DNA methylation involves adding a methyl group to cytosine residues, typically at CpG dinucleotides, regions where a guanine nucleotide follows a cytosine nucleotide. These CpG sites are often located in promoter regions of genes, and their methylation can silence or reduce the expression of associated genes, thereby regulating transcription without altering the DNA sequence[68,69]. Aberrant methylation patterns have been associated with regulating lipid metabolism, inflammation, and fibrogenesis in MASLD[70]. Various factors, including excessive caloric intake, obesity, insulin resistance, oxidative stress, and chronic low-grade inflammation, common in individuals with metabolic syndrome and MASLD, can influence these methylation changes.

Studies have identified differential methylation of genes involved in hepatic lipid regulation, such as PPARα, SREBF1, and PPARGC1A, correlating with steatosis severity and progression to MASH[71,72]. Specifically, hypermethylation or hypomethylation at regulatory regions of these genes can alter the transcription of key proteins that govern fatty acid oxidation, lipogenesis, and intracellular lipid storage. For example, altered methylation of peroxisome PPARα may reduce fatty acid β-oxidation capacity, while changes in SREBF1 methylation may enhance lipogenic gene expression, contributing to hepatic fat accumulation[71,72]. These epigenetic alterations are increasingly studied as diagnostic markers and potential therapeutic targets. Genome-wide DNA methylation profiling has enabled classification of patients with MASLD into subgroups with distinct clinical and histological features, such as differential degrees of inflammation, ballooning, and fibrosis. These subgroup profiles have demonstrated correlations with disease progression and response to dietary or pharmacologic interventions, potentially offering prognostic and therapeutic value[73].

Furthermore, methylation-based biomarkers can be assessed noninvasively through blood-derived DNA, offering a promising alternative to liver biopsy. This approach has shown potential in early diagnosis, monitoring of treatment response, and risk stratification; however, its clinical application remains at the developmental stages[74].

Despite their promise, methylation markers face challenges such as inter-individual variability, the influence of environmental factors, and the standardization of detection methods. Nonetheless, integrating epigenetic data with imaging and biochemical markers may enhance the precision of steatosis evaluation in the future.

INVASIVE METHODS FOR HEPATIC STEATOSIS EVALUATION

Liver biopsy

Liver biopsy remains the reference standard for histologically evaluating hepatic steatosis, inflammation, ballooning, and fibrosis. It allows for direct visualization of hepatic architecture and cellular changes, making it indispensable for diagnosing MASH and staging fibrosis. In research settings, a biopsy is often required for histological endpoint assessments in clinical trials[4].

The procedure is typically performed percutaneously under local anesthesia with ultrasound or CT guidance to identify a suitable site and avoid major vascular or biliary structures. The right lobe of the liver is most often targeted, usually between the seventh and ninth intercostal spaces along the midaxillary line. Patients are generally monitored for a few hours post-procedure, although in some cases, especially when there are bleeding risks or comorbidities, overnight observation or hospitalization may be required. A single pass with a core needle is often sufficient to obtain a diagnostic specimen. Still, multiple passes may be necessary to acquire an adequate sample, ideally > 20 mm and containing at least 11 complete portal tracts as recommended in clinical guidelines[4]. In an extensive review[4], the overall risk of significant complications such as bleeding was estimated at approximately 0.5%.

One of the major advantages of liver biopsy is its ability to assess multiple histopathologic features simultaneously, including the presence of mixed inflammatory infiltrates, Mallory-Denk bodies, hepatocellular ballooning, and perisinusoidal fibrosis. The NAFLD Activity Score and fibrosis staging systems, such as the Brunt or NASH Clinical Research Network scoring systems, are typically applied to quantify disease severity[4].

However, liver biopsy has several significant limitations. It is an invasive procedure associated with potential complications, including bleeding (up to 0.5%), infection, hypotension, and pain. Sampling variability is another primary concern as liver involvement in MASLD can be heterogeneous, and the small core of tissue obtained (usually 1-2 cm) may not reflect the overall liver pathology. Inter-observer variability in histological interpretation can also affect diagnostic accuracy[4].

Despite these drawbacks liver biopsy remains an essential tool in specific clinical scenarios, particularly when noninvasive modalities yield inconclusive or discordant results, or when histologic confirmation of MASH or advanced fibrosis is required. In the context of fatty liver disease, liver biopsy is typically indicated in patients with indeterminate or conflicting results from imaging and serologic markers, those with high-risk features such as diabetes mellitus or metabolic syndrome with elevated transaminases, and individuals being evaluated for participation in clinical trials targeting NASH therapeutics[11,75]. It is also recommended when autoimmune, genetic, or drug-induced liver injury is suspected but cannot be excluded noninvasively. Nonetheless, the search for accurate and reliable noninvasive alternatives has intensified recently, aiming to reduce dependence on liver biopsy in routine clinical practice.

AI in digital pathology

AI has emerged as a powerful tool in digital pathology, enabling objective and reproducible quantification of key histological features such as steatosis, inflammation, ballooning, and fibrosis. These AI-based assessments show strong agreement with expert pathologist scoring systems such as the NAFLD Activity Score and the Steatosis–Activity-Fibrosis score and provide quantitative outputs that can support both clinical trials and routine practice[76,77].

COMPARATIVE SUMMARY OF DIAGNOSTIC METHODS

Several diagnostic modalities are available for evaluating hepatic steatosis, each with unique strengths and limitations. Their performance varies across several parameters, including diagnostic accuracy, cost, invasiveness, quantitative capability, and clinical applicability.

Diagnostic accuracy

MRI-PDFF offers the highest diagnostic performance, with sensitivity and specificity typically exceeding 90% for detecting hepatic fat content ≥ 5%[6]. Attenuation-based ultrasound techniques, such as CAP, ATI, and UGAP, demonstrate high accuracy (AUROC 0.80-0.90) and have been validated primarily against MRI-PDFF and in some studies liver biopsy[20,22,27]. ATT has also demonstrated promising diagnostic accuracy (AUROC 0.79-0.85) in smaller validation studies primarily conducted in Japan[23,27].

Conventional B-mode ultrasound has limited sensitivity for detecting mild steatosis, particularly when the fat content is less than 30%[17]. In contrast, blood biomarkers such as ALT, AST, and CK-18 serve as indirect markers that reflect hepatocellular injury rather than fat accumulation per se[37,39,57].

Cost and availability

Blood tests and conventional ultrasound are widely available and cost-effective, making them suitable for primary care-setting screening. CAP and ATI require dedicated ultrasound equipment but remain more accessible than MRI, with studies supporting their clinical feasibility in routine practice[20,22]. MRI-PDFF, although highly accurate, is expensive and less widely available, limiting its use to tertiary centers or clinical trials[6]. Liver biopsy remains a resource-intensive procedure and is generally reserved for specific clinical indications[4]. Most imaging techniques and blood-based biomarkers are noninvasive. However, liver biopsy, although the reference standard for diagnosing MASH and staging fibrosis, is invasive, carries risks (e.g., bleeding), and may be subject to sampling variability[4].

Quantitative capability

MRI-PDFF enables whole-liver quantification of fat content with excellent reproducibility[6]. CAP and ATI provide semi-quantitative estimates validated against MRI-PDFF[20,27], while UGAP and ATT also offer objective measurements with good correlation to histological steatosis[23,27]. B-mode ultrasound allows only subjective grading. Blood-based biomarkers are not directly quantitative for fat content but may reflect associated processes such as inflammation or fibrosis severity[37,39,44].

Clinical applications

Due to its accessibility, ultrasound is typically used as a first-line screening tool. CAP and ATI enhance detection and quantification of steatosis in settings without MRI access[20,22,27]. MRI-PDFF is ideal for precise quantification and monitoring, especially in research and clinical trials. Liver biopsy is essential when histologic confirmation of MASH or staging of fibrosis is required, especially in patients with discordant imaging or serological results[4,11,75]. Blood-based biomarkers and composite indices (e.g., FIB-4) complement imaging by aiding in risk stratification and identifying patients who may benefit from further evaluation[11,37]. In conclusion, optimal evaluation of hepatic steatosis often requires a combination of methods tailored to the clinical context. Integrating imaging findings, serum biomarkers, and patient characteristics can enhance diagnostic accuracy and support individualized management of MASLD.

STEATOSIS REDUCTION AS A THERAPEUTIC RESPONSE MARKER

Recent interventional studies have demonstrated that reductions in hepatic fat content, particularly as measured by MRI-PDFF, serve as an important surrogate marker of therapeutic efficacy in MASLD. A relative decrease of ≥ 30% in MRI-PDFF from baseline has been associated with histologic improvement in steatohepatitis and regression of fibrosis. It is now widely used as a noninvasive endpoint in clinical trials. For example, in a multicenter study evaluating obeticholic acid for NASH, patients with ≥ 30% reduction in MRI-PDFF had a higher probability of histological improvement in ballooning and inflammation[78].

Similar observations have been reported with other steatohepatitis-treatment drugs. In a phase 2 trial of semaglutide, patients achieved significant reductions in hepatic fat content as assessed by MRI-PDFF, which were associated with improvements in histological features of steatohepatitis[79]. In another phase 2 trial, patients treated with resmetirom (MGL-3196) showed a mean reduction in liver fat content (measured by MRI-PDFF) of 32.9% at 12 weeks and 37.3% at 36 weeks, both significantly greater than the reductions observed in the placebo group (P < 0.0001). Furthermore, patients who achieved ≥ 30% fat reduction (assessed via MRI-PDFF) exhibited histological improvements, particularly in hepatocellular ballooning and inflammation[80]. These findings suggest that MRI-PDFF-based fat reduction serves as an early predictive marker of therapeutic efficacy.

While MRI-PDFF remains the most validated imaging biomarker, other modalities, such as CAP and ATI, have shown a moderate correlation with histological changes but lack the dynamic range and reproducibility needed to define clear treatment thresholds[16,32]. Blood-based markers, including CK-18, Pro-C3, and FGF21, have been explored as indicators of treatment response; however, their predictive value is variable, and they have not yet been established as standalone endpoints[44,81,82]. Epigenetic markers, including changes in DNA methylation patterns before and after intervention, are emerging as potential indicators of long-term treatment effect, although their clinical application remains investigational[8,83].

These insights underscore the growing role of fat quantification in MASLD management, not just for diagnosis but for dynamic disease monitoring and treatment stratification. Significantly, the clinical utility of hepatic fat quantification may differ based on the degree of fibrosis. In early-stage MASLD (F0-F1), liver fat content is more dynamic and responsive to lifestyle or pharmacological interventions, making it a valuable marker for treatment efficacy. In contrast, in advanced fibrosis or cirrhosis (F3-F4), hepatic fat content often declines (‘burnt-out NASH’), and its levels may not reflect disease activity or treatment response reliably. For instance, a study by Sanyal et al[84] emphasized that histological steatosis scores may decrease even as fibrosis progresses in advanced NASH. Moreover, in advanced fibrosis, histological improvements, particularly in inflammation and ballooning, may occur despite minimal changes in steatosis, suggesting that fibrosis-based metrics or composite endpoints are more appropriate in such populations.

Several studies have documented these dynamics. For example, Loomba et al[85] demonstrated that patients with advanced fibrosis often have lower steatosis by MRI-PDFF despite histological activity, indicating that fat content alone underestimates disease severity in late-stage disease. For example, a multicenter study by Caussy et al[86] showed that in patients with early-stage MASLD, steatosis reduction as measured by MRI-PDFF was strongly associated with improvements in metabolic parameters and liver function tests. Conversely, in patients without significant fibrosis, steatosis reduction as measured by MRI-PDFF or CAP correlated better with metabolic improvements and liver enzyme normalization, supporting its use as a monitoring tool in this subgroup. Therefore, fat quantification is more helpful in monitoring non-advanced disease, whereas fibrosis-based metrics become increasingly critical in later stages.

To facilitate clinical decision-making, diagnostic tools and their characteristics are summarized in Table 1, and a stepwise diagnostic algorithm for hepatic steatosis is presented in Figure 2. Table 1 classifies and compares hepatic steatosis evaluation methods. It categorizes major approaches to hepatic steatosis evaluation into histological, imaging-based, blood biomarker, and composite index methods. Each method is listed with representative examples, key advantages, and primary clinical utilities. Figure 2 shows a diagnostic algorithm for hepatic steatosis evaluation. This flowchart illustrates a stepwise approach to diagnosing hepatic steatosis based on clinical context. Screening typically begins with noninvasive methods, such as ultrasound and liver enzyme assessment (ALT/AST) combined with simple fibrosis scores (e.g., FIB-4). Patients with abnormal results proceed to more quantitative assessments using MRI-PDFF or CAP. In cases where advanced fibrosis is suspected, both serum biomarkers (e.g., CK-18, FGF21, M2BPGi, Pro-C3) and liver biopsy may be considered. Biopsy remains the reference standard for histological confirmation. Emerging investigational approaches include DNA methylation profiling and AI-based tools.

Figure 2 Diagnostic algorithm for hepatic steatosis evaluation. This flowchart illustrates a stepwise approach to diagnosing hepatic steatosis based on clinical context. Screening typically begins with noninvasive methods such as ultrasound and liver enzyme assessment (alanine aminotransferase/aspartate aminotransferase) along with simple fibrosis scores (e.g., fibrosis-4). Patients with abnormal results proceed to more quantitative assessments using magnetic resonance imaging-proton density fat fraction or controlled attenuation parameter. In cases where advanced fibrosis is suspected, both serum biomarkers (e.g., cytokeratin-18, fibroblast growth factor 21, Mac-2 binding protein glycosylation isomer, type III procollagen peptide) and liver biopsy may be considered. Biopsy remains the gold standard for histological confirmation. Emerging research-level diagnostic methods include DNA methylation profiling and artificial intelligence-based tools. ALT: Alanine aminotransferase; AST: Aspartate aminotransferase; FIB-4: Fibrosis-4; MRI-PDFF: Magnetic resonance imaging-proton density fat fraction; CAP: Controlled attenuation parameter; CK-18: Cytokeratin-18; FGF21: Fibroblast growth factor 21; M2BPGi: Mac-2 binding protein glycosylation isomer; Pro-C3: Type III procollagen peptide; AI: Artificial intelligence.

Table 1 Classification and comparison of hepatic steatosis evaluation methods.

Type	Examples	Advantages	Clinical utility	Disadvantages
Histological	Liver biopsy	Gold standard, full histological insight	Diagnosis of steatohepatitis (historically termed NASH, now MASH), fibrosis staging	Invasive, subject to sampling variability, associated with procedural risks, unsuitable for longitudinal monitoring
Imaging	US, CAP, MRI-PDFF	Noninvasive, widely available	Screening, detection, and quantification of hepatic steatosis	Limited sensitivity for mild steatosis, operator-dependent performance, reduced accuracy in obese individuals
Blood biomarkers	ALT/AST, CK-18, FGF21, M2BPGi, Pro-C3	Accessible, repeatable	Risk stratification and monitoring of fibrosis progression	Insufficient specificity, susceptible to influence by extrahepatic conditions, limited validation for staging
Composite index	FIB-4	Integrative, cost-effective	Estimation of fibrosis burden and risk assessment	Limited diagnostic specificity, not reliable as a stand-alone tool, performance influenced by age and comorbidities
Emerging technique	DNA methylation, AI algorithms	High precision, research-oriented	Exploratory application in precision diagnostics and personalized medicine	Investigational stage, relatively high cost, insufficient standardization and clinical validation

AI: Artificial intelligence; ALT/AST: Alanine aminotransferase/aspartate aminotransferase; CAP: Controlled attenuation parameter; CK-18: Cytokeratin-18; FGF21: Fibroblast growth factor 21; FIB-4: Fibrosis-4; M2BPGi: Mac-2 binding protein glycosylation isomer; MRI-PDFF: Magnetic resonance imaging-proton density fat fraction; Pro-C3: Type III procollagen peptide; US: Ultrasound; NASH: Nonalcoholic steatohepatitis; MASH: Metabolic dysfunction-associated steatohepatitis.

FUTURE PERSPECTIVES

The future of hepatic steatosis assessment lies in integrating advanced imaging, computational diagnostics, and molecular biology. These evolving approaches aim to enhance diagnostic accuracy, support early detection of disease progression, and enable personalized therapeutic interventions.

AI and machine learning are increasingly being applied to improve the interpretation of hepatic imaging, particularly ultrasound and MRI. Deep learning models such as convolutional neural network models have demonstrated high diagnostic accuracy in assessing steatosis. Han et al[14] reported that AI-assisted quantitative ultrasound achieved an AUROC value exceeding 0.90 for detecting steatosis in NAFLD, and Byra et al[33] demonstrated that transfer learning applied to ultrasound images could accurately classify steatosis severity.

MRI-based AI applications have also advanced notably. For instance, Wu et al[87] developed an automated whole-liver fat quantification method using MRI-PDFF maps. The method demonstrated strong correlations with manual region-of-interest measurements and magnetic resonance spectroscopy (r ≈ 0.96), suggesting high feasibility for clinical and research applications. Wang et al[88] systematically reviewed applications of deep learning and radiomics in liver imaging, highlighting their utility in quantifying steatosis, staging fibrosis, and evaluating tumors.

Several recent studies have explored deep learning-based radiomics for prognostic purposes in HCC. Wei et al[89] developed an automated MRI segmentation model to predict early recurrence of HCC, while Zhao et al[90] reported that contrast-enhanced MRI combined with AI could stratify recurrence risk preoperatively.

Although these approaches have primarily focused on HCC and fibrosis, similar AI-based frameworks are now being investigated in MASLD. Integrating imaging biomarkers with clinical and molecular data may help identify treatment responders, monitor disease activity, and support personalized management strategies. These developments underscore AI’s expanding role in diagnosis, prognostication, and individualized treatment planning, with growing applicability to MASLD.

Multiomics and systems biology approaches are increasingly shedding light on the heterogeneity of MASLD. By integrating transcriptomic, metabolomic, and proteomic data with clinical and imaging findings, researchers aim to classify MASLD into molecular subtypes with distinct prognostic and therapeutic profiles. Metabolomic signatures, particularly those involving branched-chain amino acids and lipid metabolites, have been linked to the progression of steatohepatitis[91]. In parallel, transcriptomic analyses have identified immune-metabolic dysregulation patterns associated with fibrosis severity. Such integrated multiomics profiles may facilitate patient stratification and help guide personalized treatment strategies[92].

Epigenetic diagnostics, particularly DNA methylation assays using blood-derived cell-free DNA, are being explored as minimally invasive alternatives to liver biopsy. Aberrant methylation of genes regulating lipid metabolism and fibrogenesis has been implicated in NAFLD/MASLD progression. For example, methylation changes in the PGC1a promoter have been associated with insulin resistance and fibrosis severity in NAFLD patients[72]. Genome-wide methylome analyses have also identified altered methylation in genes regulating lipid metabolism, including PGC1a and SREBF1, correlating with disease progression[73]. Hardy and Mann[74] further summarized how epigenetic alterations contribute broadly to liver disease pathogenesis and highlighted their potential as therapeutic targets. Methylation signatures in additional genes, such as SREBF1 and PNPLA3, have also been implicated. Wu and Liu[93] reported altered methylation of SREBF1 in an NAFLD model, and Ma et al[94] identified a peripheral blood methylation signature at the PNPLA3 locus linked to hepatic fat accumulation. Furthermore, Pan et al[95] found that aberrant methylation in PRKCE and SEC14 L3 promoters could distinguish patients with NAFLD from controls. While clinical implementation remains in early stages, commercial assays based on methylation-sensitive PCR or next-generation sequencing platforms are under development. These biomarkers may soon support longitudinal disease monitoring or therapeutic stratification in MASLD.

Challenges remain, including large-scale validation, standardization of assay platforms, and integration of omics-based risk scores into clinical workflows. Furthermore, ethical and economic considerations must be addressed, particularly regarding data privacy, cost-effectiveness, and accessibility in under-resourced healthcare systems[11].

Population-level validation and infrastructure are essential for facilitating the clinical implementation of omics-based diagnostics. In the United States, the NASH Clinical Research Network has been instrumental in integrating omics data into MASLD research and stratified trial design[96]. Similarly, in Europe, the Elucidating Pathways of Steatohepatitis consortium has contributed significantly to understanding the pathogenesis of steatohepatitis through omics approaches[96]. Japan has also been actively contributing to these advancements with studies like the NAFLD in Gifu Area, providing insights into the natural history and risk factors of NAFLD in the Japanese population[97]. In conclusion, the convergence of AI, omics technologies, and liquid biopsy approaches holds promise for a new era of precision hepatology. Future MASLD evaluation will likely rely on integrated diagnostic platforms that deliver personalized insights from a single blood sample and an image.

Immune-related mechanisms may influence both disease progression and diagnostic performance. For example, Mendelian randomization analysis has demonstrated causal roles of immune cell subsets in cirrhosis[98]. Demographic and epidemiologic factors, including sex-specific differences in cirrhosis burden, further underscore the importance of tailoring diagnostic strategies to diverse populations[99]. Beyond MASLD, mendelian randomization studies have also linked immune cell phenotypes[100] and gut microbiota[101] to the risk of biliary tract cancer, highlighting the interdisciplinary implications of hepatology research.

CONCLUSION

Hepatic steatosis, particularly in MASLD, is an escalating global health concern. Precise assessment is crucial for diagnosis, risk stratification, therapy guidance, and disease progression monitoring. While conventional ultrasound remains a first-line tool due to its accessibility, advanced imaging modalities such as MRI-PDFF and CAP offer superior quantification. Blood-based biomarkers, including emerging epigenetic indicators, provide complementary molecular insights.

Although liver biopsy remains a reference standard in select cases, the paradigm is shifting toward noninvasive, integrated diagnostic strategies that combine imaging, serum biomarkers, and multiomics data. Future frameworks are already beginning to incorporate AI and machine learning to enable more individualized and real-time evaluation.

Ultimately, the assessment of hepatic steatosis is evolving from static and invasive procedures to dynamic, personalized, and scalable diagnostics. To realize this vision, robust validation across populations and clinical settings is imperative along with coordinated international efforts to standardize tools and ensure broad accessibility. Such innovations hold the promise of transforming clinical outcomes in MASLD worldwide.