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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5533_Библиотеки_им_академика_М_И_Перельмана.pdf
Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
Figure 8.2. An overview of the diagnosis and therapy selection using AI in disease treatment. Created via
Biorender
imaging, aiding in the early detection of hepatocellular carcinoma [6]. AI systems
can also analyze laboratory results and clinical data to identify potential risk factors
for liver disease, enabling earlier intervention and management. Furthermore, AI
has the ability to enhance prognostic assessment in hepatitis and chronic liver
disease [7]. By analyzing various patient factors and disease characteristics, AI
algorithms can generate predictive models to estimate disease progression, treatment
response, and overall patient outcomes, which can be understood with the help of
figure 8.2. Clinical professionals may use this data to create personalized treatment
plans, spot high-risk patients who can benefit from more frequent follow-up or early
intervention, and maximize the use of healthcare resources.
AI has the potential to improve therapy choices and efficiency in addition to
diagnosis and prognosis. AI systems may recognize trends in therapy response
through data analysis and machine learning, assisting medical professionals in
selecting the best suitable treatments based on specific patient profiles [8]. This can
minimize trial-and-error approaches, reduce treatment costs, and improve patient
outcomes. AI can also assist in monitoring treatment response over time, enabling
timely adjustments and personalized interventions.
To fully utilize AI in hepatology, further research and partnerships between AI
developers and medical practitioners are required as technology develops. We can
significantly advance the treatment of liver illness and, eventually, decrease the impact
of hepatitis and chronic liver disease worldwide by leveraging the potential of AI [9].
8.2 Artificial intelligence role in hepatitis disease
Hepatitis, a global public health concern, is an inflammation of the liver caused by
viral infections (hepatitis A, B, C, D, and E), alcohol abuse, drug toxicity,
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autoimmune disorders, or metabolic diseases. Globally, it affects millions of
individuals and can cause serious side effects such as liver cirrhosis and hepatocellular cancer. Once the patient develops liver cirrhosis they will experience several
other health-related issues such as ascites, jaundice, red plams, etc. Figure 8.3 shows
health issues caused by cirrhosis. The accurate and timely diagnosis of hepatitis,
along with effective treatment strategies, is crucial for reducing its impact and
improving patient outcomes. In recent years, AI has stepped up to this challenge,
offering invaluable contributions across different stages of the disease continuum.
Hepatitis diagnosis and treatment is one of several significant domains where AI
has had an important contribution. The identification of high-risk groups and the
implementation of targeted treatments have been challenges for public health
authorities and organizations globally [10]. AI algorithms, however, have the
capability to analyze vast amounts of demographic, behavioural, and genetic data
to pinpoint individuals at higher risk of contracting the virus. Health officials may
now concentrate on preventative efforts like vaccination drives and awareness
campaigns that can significantly lower the prevalence of hepatitis thanks to this
knowledge [11].
Additionally, patient involvement and education are changing thanks to chatbots
and virtual assistants driven by AI. These interactive systems can provide individualized information, respond to patient questions, and encourage commitment to
medications. AI leads to better patient outcomes and an overall improvement in
quality of life by allowing individuals to successfully manage their disease [12].
AI has a pivotal role in patient monitoring and disease management. Wearable
devices and remote monitoring tools equipped with AI can track liver function, and
other relevant parameters, providing real-time data to healthcare professionals.
This continuous monitoring allows for early detection of disease exacerbation and
prompt intervention, preventing disease progression and reducing hospitalizations.
Moreover, by personalizing medicines for each patient, AI-driven personalized
medicine is transforming the treatment of hepatitis. Hepatitis viruses can mutate
Figure 8.3. Health issues caused by cirrhosis of liver in patient. Created via Biorender
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Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
rapidly, leading to drug resistance and treatment failure. AI algorithms, however,
can continuously analyze a patient’s viral genetic data and treatment response,
enabling real-time adjustments to medication regimens. This approach enhances
treatment efficacy and minimizes adverse effects, optimizing patient outcomes and
reducing the burden on healthcare systems [13, 14].
Nevertheless, despite these remarkable developments, integrating AI into hepatitis care doesn’t come without difficulties. As AI systems depend on a significant
quantity of sensitive medical data, maintaining patient data privacy and security as a
top priority is necessary. To win over the public and ensure that AI technologies are
widely used in healthcare, it is essential to strike a balance between utilizing the
promise of AI and protecting patient privacy.
8.2.1 Limitations of the traditional method for the diagnosis and treatment of
hepatitis disease
Although widely used for many years, conventional approaches for the diagnosis
and treatment of hepatitis have certain major drawbacks that restrict their
effectiveness and overall influence on patient outcomes. These flaws have motivated
the investigation of novel strategies, including the use of AI and cutting-edge
technology, to get over these obstacles and enhance hepatitis management.
Firstly, the sensitivity and specificity necessary for accurate and early detection
are sometimes lacking in the usual diagnostic procedures for hepatitis, such as
serological testing and liver biopsies [15]. Even though serological tests are
frequently used to identify viral antigens and antibodies, they may result in false
negatives when the virus is present but no antibodies have yet formed. Similar to
blood tests, liver biopsies are intrusive, expensive, and risky, making them
unsuitable for regular monitoring and follow-up. However, they are considered to
be the gold standard for determining liver damage and staging hepatitis. These
limitations can delay diagnosis and impede timely intervention, potentially allowing
the disease to progress to more severe stages.
Moreover, antiviral drugs are the foundation of traditional hepatitis treatment
methods, which can be helpful but also have side effects. These medications often
target specific viral components, making them susceptible to drug resistance as the
virus mutates over time. Also, the treatment plans are frequently uniform, making it
difficult to adapt them to the special traits and treatment reactions of each patient.
As a result, some patients may experience suboptimal treatment outcomes or
develop complications due to the inability to adapt therapy based on their specific
needs [16]. Another limitation lies in the monitoring and follow-up of patients with
hepatitis. Periodic clinic visits and laboratory tests are commonly used for disease
assessment, but they may not provide a real-time and continuous evaluation of a
patient’s condition. This kind of infrequent monitoring might overlook slight
alterations in the course of the disease or the effectiveness of the treatment, delaying
therapeutic modifications or obstructing possibilities for early intervention.
Therefore, in order to ensure improved disease management, more dynamic and
patient-centred monitoring methods are required [17].
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The conventional approaches to diagnosing and treating hepatitis suffer from a
lack of scalability and accessibility, especially in areas with few resources. Liver
biopsies, as mentioned earlier, require specialized facilities and expertise, making them
challenging to implement in certain regions [18]. In the same way, access to other
common treatment options may be restricted in some places, depriving patients of the
care they require [19]. These variations in access to healthcare can increase the burden
of hepatitis globally and prevent successful attempts to manage the virus.
8.2.2 Artificial intelligence in hepatitis diagnosis
Accurate diagnosis is the cornerstone of effective disease management. The use of AI
methods, in particular machine learning and deep learning algorithms, has shown
considerable potential for improving hepatitis B and hepatitis C diagnosis. This has
changed the way medical practitioners handle this important component of patient
care. Medical image analysis is one of the main uses of AI in the diagnosis of hepatitis
[30]. AI algorithms, particularly those based on deep learning and convolutional
neural networks, seen in figure 8.4, have shown remarkable performance in interpreting liver imaging modalities such as ultrasound, computed tomography (CT), and
magnetic resonance imaging (MRI). This assists radiologists and clinicians in making
accurate and timely diagnoses [30]. These algorithms can automatically detect and
segment liver lesions, assess liver texture and parenchymal changes, and identify signs
of fibrosis and cirrhosis, which are common manifestations of chronic hepatitis. Early
diagnosis of liver problems is made possible by AI’s capacity to quickly and reliably
assess huge quantities of imaging data, which enables prompt intervention and better
patient outcomes. In addition to medical imaging, AI plays a pivotal role in
interpreting laboratory test results used in hepatitis diagnosis. In order to determine
the presence of viral infection and distinguish between various kinds of hepatitis,
serological tests that look for viral antigens and antibodies are important [12]. AI
algorithms can process vast databases of serological test results, along with clinical
information from electronic health records, to identify patterns that signify hepatitis
infection and assess disease severity. AI-powered diagnostic tools can offer medical
practitioners insightful information and evidence-based suggestions for patient treatment by combining this data-driven methodology with domain-specificexpertise.
Figure 8.4. AI has been divided into two primary categories: machine learning and deep learning. Created via
Biorender
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Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
Investigators have used artificial neural network (ANN), as artificial intelligence
paradigms, to provide a reliable outcomes for clinical problems. An ANN is a
mathematical model, which is inspired by the biological nervous system. Like in
Nature, how a network functions is primarily determined by connections between its
components. Through learning, ANNs may detect intricate patterns between inputs
and outputs [20]. Also, AI has proven to have excellent abilities in predicting
hepatitis growth and treatment response. Using machine learning algorithms,
chronic patient data may be combined and evaluated, including laboratory results,
imaging results, and clinical outcomes [21]. AI can help doctors create individualized
treatment strategies for specific individuals by analyzing illness progression trends
and factors affecting treatment success. This precision medicine approach optimizes
therapeutic efficacy while minimizing adverse effects, thereby enhancing patient
well-being and treatment adherence [21].
Risk prediction modelling is an important field where AI is effective in the
diagnosis of hepatitis. By analyzing various risk factors, including demographic
data, lifestyle habits, comorbidities, and genetic predispositions, AI algorithms can
assess an individual’s likelihood of developing hepatitis or experiencing disease
progression. These risk prediction models allow for focused screening and early care
for high-risk patients, decreasing the total impact of hepatitis on public health
systems and averting disease consequences [22].
Hepatitis diagnosis is made even more simple by the incorporation of AI into
electronic health record (EHR) systems, which aggregate and analyze huge amounts
of patient data. Doctors may be alerted to probable hepatitis cases and assisted in
making better decisions by AI-powered EHRs, which can automatically identify
critical clinical markers and risk factors. The ef ficiency of the medical system is
improved, diagnostic mistakes are decreased, and patients receive a higher level of
treatment because of this thorough and data-driven approach [23].
The influence of AI technology on hepatitis detection is projected to increase as it
develops and becomes more widely available, significantly advancing efforts to
battle and manage this serious public health issue on a worldwide scale.
8.2.3 Treatment and management of hepatitis with artificial intelligence
The choice of the best possibilities for therapy is essential when hepatitis is identified.
With the incorporation of AI, there has been a paradigm change in the treatment
and management of hepatitis, providing creative solutions that optimize therapeutic
approaches, improve patient outcomes, and expedite healthcare delivery [24]. A
thorough and customized strategy is necessary to treat the various forms and varied
disease regimens of hepatitis, a viral infection of the liver. Medical professionals may
now offer individualized therapies, track patient progress, and forecast treatment
outcomes with better accuracy and efficiency thanks to AI-driven technologies,
which have emerged as potent tools in this field [24].
Drug research and development are some of the main implications of AI in the fight
against hepatitis. AI has changed this field of research by speeding up the identification
of prospective antiviral drugs, which was previously a time- and money-consuming
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Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
procedure. AI algorithms can analyze libraries of molecular structures and predict the
interaction between viral components and drug candidates with remarkable accuracy
[25]. By simulating drug-receptor interactions, AI narrows down the list of potential
drug candidates, accelerating the development of new antiviral therapies. This strategy
makes it possible to investigate innovative therapy alternatives, address drug resistance,
and increase the number of drugs accessible for the treatment of hepatitis. AI’s potential
extends beyond drug discovery to precision medicine, Because hepatitis viruses may
change quickly, different individuals may respond differently to therapy [26]. In order to
find trends that affect medicine effectiveness, AI can continually monitor patient data,
including viral genetic data and treatment outcomes. AI systems that use machine
learning can forecast the best treatment plans for specific patients by taking into account
aspects like medication resistance, liver function, and allergies. This targeted strategy
improves patient compliance and treatment outcomes while minimizing side effects and
maximizing treatment effectiveness [26].
AI algorithms can find prognostic markers linked to disease development or
remission by examining massive datasets of patient records, laboratory findings, and
clinical outcomes. Thanks to these forecasting capabilities of AI, doctors may
undertake early treatments and preventative measures, reducing illness complications and improving long-term results [27]. Additionally, AI has shown to be quite
helpful in assisting medical professionals in making difficult treatment decisions.
Clinical decision-support systems powered by AI combine patient data with the
most recent scientifi c findings and clinical advice to provide real-time suggestions on
possible treatments, dose modifications, and medication interactions. These decision-support tools improve medical judgment, lower the chance of mistakes, and
encourage evidence-based practice [28].
AI has played an essential part in dealing with the management of hepatitis in
public health in addition to treatment. An immense amount of demographic and
illness data can potentially be analyzed by AI-powered epidemiological models to
forecast disease outbreaks, estimate the impact of diseases, and guide public health
initiatives. In order to manage and eradicate hepatitis as a hazard to the general
population, the world is making efforts to do so. This forecasting skill helps with
resource allocation, vaccine planning, and disease preventive activities [29]. AI has
the potential to significantly improve hepatitis management and change medical
procedures for the benefit of the millions of people who are impacted by this disease,
provided that research is conducted and deployment is done responsibly.
Despite these amazing developments, integrating AI in the management and
treatment of hepatitis does present some difficulties, such as establishing confidence
in AI-driven healthcare solutions from the public.
8.2.4 Artificial intelligence-enabled hepatitis disease surveillance and prevention
AI has the potential to be extremely useful in disease surveillance and preventive
initiatives in addition to diagnosis and treatment. AI systems can identify the latest
developments and early warning indications of hepatitis outbreaks by analyzing vast
amounts of information that come from EHRs, health department records, and
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social media. In order to slow the spread of the disease, public health officials can use
this early diagnosis to assist them execute immediate actions and preventative
measures, such as vaccination programs [21]. With the goal to identify early warning
signs of future hepatitis outbreaks or vaccination reluctance, AI-driven systems can
examine the language used in online conversations. Public health authorities may
better focus programs and campaigns to increase awareness, encourage immunization, and dispel myths about hepatitis by using this relevant information [30].
AI plays a critical role in organizing vaccination activities for the prevention of
disease. AI-powered algorithms are able to detect high-risk populations with low
immunization rates by analyzing the population’s demographics, illness frequency,
and vaccine coverage rates [31]. These predictive models give healthcare systems the
ability to spend resources proactively, create focused preventative plans, and
forecast future healthcare requirements. Public health officials may use this
information to specifically target outreach and vaccination initiatives, ensuring
that hepatitis is prevented in susceptible groups [30].
AI-driven simulations can also forecast how the virus may change over time,
enabling the creation of vaccinations that offer broader and more durable protection. AI may also monitor vaccination outcomes in real-time to evaluate the efficacy
of currently available vaccines, enabling quick revisions to immunization plans as
necessary [32]. AI-enabled surveillance is essential for tracking hepatitis-related
complications and determining the severity of the condition. AI can uncover
patterns of life-threatening cases and consequences by examining hospitalization
data and clinical outcomes, leading focused interventions and treatment approaches.
This thorough understanding of the disease’s impact helps medical organizations to
better manage resources and improve patient care [33].
Furthermore, contact tracing, a crucial part of disease prevention during outbreaks, is being transformed by AI. Machine learning AI-powered contact tracking
systems can quickly find and alert people who may have been exposed to the virus,
allowing for quick testing and action to stop future transmission [7]. AI simplifies the
discovery of possible transmission chains by automating contact tracking procedures, effectively interrupting the cycle of illness.
8.3 Artificial intelligence role in non-alcoholic fatty liver disease
Millions of people worldwide are affected by Non-Alcoholic Fatty Liver Disease
(NAFLD), which has become a serious global health problem. The term ‘NAFLD’
refers to a group of diseases where excessive amounts of fat build-up in the liver,
causing inflammation and liver cell destruction as well as the possibility of
developing into more severe stages including cirrhosis, hepatocellular carcinoma,
and fibrosis [34]. Innovative methods are urgently needed in order to help with
NAFLD’s early detection, precise diagnosis, and personalized treatment due to its
increasing frequency. AI, which has the potential to completely transform the
healthcare industry, has recently emerged as a promising technology in the medical
industry. With a focus on its uses, difficulties, and potential uses, AI is examined in
this chapter’s discussion of NAFLD [35].
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The global obesity epidemic and the development of metabolic diseases like
diabetes and dyslipidemia are the main causes for why NAFLD has become the
most frequent cause of chronic liver disease globally. The burden of NAFLD extends
beyond liver-related complications, as it is closely associated with an increased risk of
cardiovascular disease, type 2 diabetes, and overall mortality. However, the early
detection and accurate diagnosis of NAFLD remain challenging, hindering effective
disease management and the prevention of disease progression [34].
The multidisciplinary discipline of computer science known as AI has shown
incredible promise for use in medical fields. It entails the establishment of computer
systems that are capable of carrying out operations like learning, analyzing situations,
and coming up with solutions that would ordinarily need human intelligence [36]. Due
to their capacity to analyze complicated data and extract useful information, machine
learning and deep learning, the two subfields of AI, have become more popular. One
of the key applications of AI in NAFLD lies in the early detection and diagnosisof the
disease. Medical imaging techniques, such as ultrasound, CT, and MRI, play a crucial
role in assessing liver fat content and distinguishing between simple steatosis and nonalcoholic steatohepatitis (NASH) [37]. Manually interpreting these photos, however,
may be difficult and time-consuming. By precisely assessing and quantifying liver fat
levels, AI algorithms have the ability to automate this procedure, resulting in quicker
and more accurate diagnoses. Additionally, AI can help medical professionals in
differentiating between NASH and simple steatosis, allowing them to identify
individuals who are more likely to experience disease progression and develop specific
therapies.
AI can assist in making use of NAFLD prediction models in addition to diagnostics.
AI algorithms may find patterns and relationships in massive datasets of data from
hospitals and laboratories that may not be obvious to human observers [38]. By
calculating a person’s probability of acquiring NAFLD and accompanying problems,
these predictive models enable early intervention and preventative actions. Additionally,
in order to offer targeted treatment suggestions, AI algorithms can incorporate patientspecific data, such as demographic, genetic, and lifestyle characteristics. The potential
benefits of this personalized strategy include better patient care, better treatment results,
and an eventual reduction in the overall condition of NAFLD [39].
Despite the positive potential of AI in NAFLD, there are still a number of
obstacles to overcome. For the establishment of reliable AI models, high-quality,
well-annotated data must be accessible. However, disparities in data completeness,
uniformity, and quality among healthcare organizations and systems provide
problems for data-driven projects. Moreover, rigorous validation studies and
integration into existing clinical workflows are essential to evaluate the performance
and effectiveness of AI algorithms in real-world settings.
8.3.1 Limitations of the traditional method for the diagnosis and treatment of
non-alcoholic fatty liver disease
NAFLD is a complex liver disorder characterized by the accumulation of fat in the
liver. It covers a broad spectrum of diseases, including simple steatosis, NASH,
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fibrosis, cirrhosis, and even hepatocellular cancer. Accurate diagnosis of NAFLD is
essential for appropriate management and intervention. Traditional diagnostic
techniques for NAFLD, however, have a number of drawbacks that reduce their
efficacy and dependability.
Liver biopsy is a frequent yet expensive technique of NAFLD diagnosis. A tiny
tissue sample from the liver is taken as part of this process for microscopic analysis.
Although liver biopsy is regarded as the most reliable method for NAFLD diagnosis
and staging, it has a number of limitations. First of all, it is an intrusive operation
that has risks including bleeding, pain, and infection. This restricts its usage,
especially in individuals who are hesitant to undergo an invasive operation or
who have underlying medical issues. Because the medical condition may not be
spread evenly throughout the liver, liver biopsy is also sensitive to sample heterogeneity [40]. As a result, the diagnosis may be affected if the biopsy sample fails to
properly represent the general status of the liver. Furthermore, liver biopsy is more
difficult to access, particularly in areas with low resources, as it needs specialized
facilities, trained workers, and expensive equipment. Imaging techniques, such as
ultrasonography (US), CT scan, and MRI, are also utilized for the diagnosis of
NAFLD [37]. Due to its low cost and non-invasive nature, ultrasonography is
commonly utilized. However, it is not always reliable in distinguishing between mild
and moderate hepatic steatosis. Furthermore, it might not be sensitive enough to
spot early-stage fibrosis or inflammation, both of which are essential signs of NASH.
In comparison to ultrasonography, a CT scan can reveal more specific information
about the amount of fat and fibrosis in the liver [41]. However, it involves ionizing
radiation and is relatively expensive. The associated radiation exposure risks limit its
use for routine screening purposes. In determining the amount of liver fat present
and distinguishing between steatosis and NASH, MRI, especially magnetic resonance spectroscopy (MRS), provides incredible precision. The extensive use of MRI
for regular NAFLD screening and monitoring is nonetheless constrained by its high
cost, restricted availability, and time-consuming nature [42].
Blood tests are frequently used to diagnose NAFLD, including those that
measure liver enzymes and certain biomarkers. For the purpose of evaluating liver
function, it is common practice to monitor liver enzymes such as alanine aminotransferase (ALT) and aspartate aminotransferase (AST). Although these enzymes
lack specificity, many NAFLD patients, especially those with pure steatosis, may
have levels that are within the normal range [41]. Because of this, depending just on
liver enzymes may result in a missed or postponed diagnosis of NAFLD. Blood tests
and biomarkers may not be able to offer a thorough evaluation of the severity of a
disease, despite being advantageous for screening. Another strategy is to utilize
composite scores, such as the Fatty Liver Index (FLI), which determines a score
based on factors including body mass index, waist circumference, triglyceride levels,
and gamma-glutamyl transferase (GGT). Although the FLI can assist in identifying
those who are at risk for NAFLD, it is not a reliable diagnostic tool and cannot
distinguish between various disease stages. Similar to this, non-invasive fibrosis
biomarkers to calculate the degree of fibrosis have been established, such as the
NAFLD Fibrosis Score (NFS) and Fibrosis-4 (FIB-4) index. These results, however,
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may need to be confirmed by further testing or imaging as they are not totally
accurate. The diagnosis and treatment of NAFLD may be improved by technological developments such as transient elastography, advanced MRI sequences, and
new blood biomarkers [43].
8.3.2 Artificial intelligence in non-alcoholic fatty liver disease diagnosis
The field of medical diagnostics has seen the emergence of AI as a potent tool that is
altering how diseases are identified and treated. The diagnosis of NAFLD, a medical
condition marked by the build-up of fat in the liver of people who drink little to no
alcohol, is one area where AI has shown substantial promise. A significant portion of
the world’s population now suffers from NAFLD, which must be diagnosed correctly
and early in order to be effectively managed and prevented from progressing to more
serious liver conditions including cirrhosis and hepatocellular carcinoma [44]. NAFLD
is often diagnosed through invasive techniques like liver biopsies, which are not only
costly and time-consuming but also run the risk of consequences. AI-based approaches
use cutting-edge algorithms and machine learning to evaluate medical data and produce
precise diagnoses. These methods are non-invasive and effective. The capability of AI to
rapidly analyze vast amounts of data and discover patterns that human observers might
miss is one of the major benefits of AI in NAFLD diagnosis [34].
Medical imaging plays a crucial role in the diagnosis of NAFLD, and AI has
demonstrated its potential in this area. For instance, AI algorithms may examine
ultrasound, CT, and MRI images to pinpoint certain markers connected to
NAFLD, such as liver fat content, fibrosis, and inflammation. AI models may
learn to detect these patterns with high accuracy by training on massive datasets of
medical pictures, enabling radiologists to make more accurate diagnoses. By doing
so, medical staff are freed up to concentrate on other important activities while
simultaneously increasing the efficiency of diagnosis [45].
Medical imaging is only one sort of data that AI may use to improve NAFLD
diagnosis; other categories include patient histories, test findings, and genetic data.
AI models may create detailed patient profiles and produce unique risk evaluations
for NAFLD by combining these data sources. This makes it possible to identify
those who are at a high risk of contracting the disease early on, permitting suited
therapies and lifestyle changes to stop its progression. Additionally, AI algorithms
are capable of ongoing learning and adaptation based on actual patient outcomes,
which helps them develop their diagnostic abilities over time and provide better
patient care. Another area where AI has made significant contributions to NAFLD
diagnosis is in the development of predictive models. AI systems can scan big
datasets comprising a variety of patient data to identify risk factors and forecast the
possibility of NAFLD onset or progression by utilizing machine learning techniques. These models can support doctors in making well-informed decisions about
patient management and treatment plans, maximizing healthcare resources, and
raising patient satisfaction [46].
Additionally, AI-driven decision-support systems can help medical professionals
understand complicated data and navigate the enormous amount of NAFLD-related
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