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- •Acknowledgements
- •Chapter 3
- •Chapter 4
- •Chapter 5
- •Chapter 6
- •Chapter 7
- •Editor biographies
- •Vivek Kumar Chaturvedi
- •Anurag Kumar Singh
- •Jay Singh
- •Dawesh Prakash Yadav
- •Short description about chapters
- •Chapter 1
- •Chapter 2
- •Chapter 8
- •Chapter 9
- •Chapter 10
- •Chapter 11
- •Chapter 12
- •List of contributors
- •Introduction
- •1.1 Introduction
- •1.2 Nanotechnology in medical science
- •1.2.1 Nanomaterials in drug delivery
- •1.2.2 Use of nanomaterials in designing diagnostic nanosensors
- •1.2.3 Nanomaterials as theranostics
- •1.3 Artificial intelligence in medical science
- •1.3.1 Machine learning in diagnostics
- •1.3.2 Natural language processing in healthcare
- •1.3.3 Predictive analytics in patient care
- •1.4.1 Nanoscience in controlled drug release in the GI tract
- •1.4.3 Nanotechnology in gastrointestinal endoscopy
- •1.4.4 Nano-biotechnology in gastrointestinal cancer
- •1.5 Role of nanoparticles for the treatment of gastric cancer
- •1.6 Artificial intelligence in hepatitis and chronic liver disease
- •1.7 Artificial intelligence applications for clinical decisions support
- •1.9 Nanomedicines for liver fibrosis
- •1.10 Artificial intelligence-based colonoscopy
- •1.12 Summary and conclusions
- •Acknowledgments
- •References
- •2.1 Introduction
- •2.2 Causes
- •2.3 Mechanism
- •2.4 Diagnosis
- •2.5 Prognosis
- •2.6 Present methods of detection
- •2.7 Biosensors
- •2.7.1 Components of biosensors
- •2.7.2 Types of biosensors
- •2.7.3 Enzyme based biosensors
- •2.7.5 Immunosensors
- •2.7.6 Microbial biosensors
- •2.7.7 DNA-based biosensors
- •2.7.8 Phage sensors
- •2.7.9 Optical biosensors
- •2.7.10 Cantilever-based biosensors
- •2.7.11 Bio-MEMS
- •2.8 Physical biosensors
- •2.8.1 Thermometric biosensors
- •2.8.2 Acoustic biosensors
- •2.8.3 Magnetic biosensors
- •2.8.4 Wearable skins as biosensors
- •2.9 Electrochemical biosensors
- •2.9.1 Potentiometric
- •2.9.2 Coulometry methods
- •2.9.3 Conductometry methods
- •2.9.4 Potentiometric titration
- •2.10 Materials for biosensors
- •2.10.1 Nanomaterials for biosensors
- •2.10.2 Gastrointestinal diseases (GIDs) biosensor
- •2.11 Summary and future perspectives
- •3.1 Introduction
- •3.2 Challenges in drug delivery to the GI tract
- •3.2.1 Residence time
- •3.2.4 Metabolism in the GI tract
- •3.3 Role of nanoscience in drug delivery
- •3.3.2 Targeted drug delivery
- •3.3.3 Increased bioavailability
- •3.3.4 Reduced toxicity and side effects
- •3.3.5 Imaging and diagnostic capabilities
- •3.3.6 Drug designing
- •3.3.7 Delivery system
- •3.4 Methods of nanomedicine formulation
- •3.5 Drug release strategies
- •3.5.1 Active targeting strategies
- •3.5.2 Stimuli-based delivery strategy
- •3.5.3 pH-dependent drug release
- •3.5.4 ROS-dependent drug release
- •3.5.5 Time-dependent dosage forms
- •3.5.6 Gastro retentive strategies
- •3.5.7 Photothermal and photodynamic approach
- •3.6 Types of nanoparticles in drug delivery
- •3.6.1 Liposomes
- •3.8 Application of AI in GI disease
- •3.9 Future perspectives and challenges
- •3.6.2 Polymeric nanoparticles
- •3.6.3 Metallic nanoparticles
- •3.6.4 Quantum dots
- •3.7 Approved nanomedicines
- •3.9.1 Diagnostics
- •3.9.2 Individualized treatment
- •3.9.3 Proactive patient monitoring
- •3.9.4 Decision support systems
- •3.9.5 Biomarker discovery and therapeutic development
- •3.9.6 Patient outcomes and quality of life
- •3.9.7 Regulation and ethical issues
- •3.10 Conclusion
- •References
- •4.1 Introduction
- •4.2 Challenges and barriers in drug delivery
- •4.3 Drugs used in IBD
- •4.4 Novel drug delivery system for inflammatory bowel disease
- •4.4.1 Vesicular delivery system
- •4.4.2 Nanoparticle drug delivery system
- •4.5 pH-dependent nano-delivery systems
- •4.6 Inorganic nanoparticles
- •4.7 Prodrugs based
- •4.8 Hybrid drug delivery systems
- •4.9 Enteric coated formulations
- •4.10 RNA interference-based novel drug delivery
- •4.11 Toxicity profiling of IBD
- •4.11.1 Corticosteroids
- •4.11.2 Immuno modulators
- •4.11.3 Biologic therapies
- •4.11.4 JAK inhibitors
- •4.11.5 Immune dysregulation in IBD
- •4.11.6 Gastrointestinal effects
- •4.11.7 Antibiotics
- •4.11.8 Cyclosporine
- •4.11.10 Surgery-related complications
- •4.11.11 Increased risk of colorectal cancer
- •4.12 Current prospective of IBD
- •4.12.1 Personalized medicine and immunological therapies
- •4.12.2 Disease monitoring and surgical advances
- •4.12.3 Development of IL-6 signaling inhibitors
- •4.12.4 Genome-wide association studies (GWAS)
- •4.12.5 Rare variant analysis
- •4.12.6 Functional genomics and gene expression studies
- •4.12.7 Therapeutic targets
- •4.12.8 Gene-environment interactions
- •4.13 Future prospective of IBD
- •4.13.2 Microparticles-based delivery systems
- •4.13.3 Biological therapies
- •4.13.4 Combination therapies
- •4.14 Conclusion
- •References
- •5.1 Introduction
- •5.2 Nanotechnology
- •5.3 Nanoparticles
- •5.4 Classification of nanoparticles
- •5.4.1 Polymer-based nanoparticles
- •5.4.2 Solid nanoparticles
- •5.4.3 Carbon-based nanoparticles
- •5.4.4 Lipid-based nanoparticles
- •5.4.5 Nanoemulsions
- •5.4.6 Nanoparticles in biomedical applications
- •5.4.7 Characteristics of nanoparticles
- •5.4.8 Characterization of nanoparticles
- •5.5 Intestinal endoscopy
- •5.6 Medical nanotechnology
- •5.6.1 Diagnosis
- •5.6.2 Nanotechnology in the early diagnosis
- •5.6.3 Theragnostic
- •5.6.4 Tissue engineering
- •5.6.5 Targeted imaging and therapeutic in colorectal cancer
- •5.6.6 Gene therapy delivery
- •5.6.7 Colitis therapy
- •5.6.8 Oral delivery of vaccines
- •5.6.9 Mitigation
- •5.6.10 Role in targeted drug delivery
- •5.7 Role of nanotechnology in intestinal tract
- •5.8 Nanotechnological aids
- •5.8.1 Nanopowder
- •5.8.2 Plastic stents
- •5.8.3 Capsule endoscopy
- •5.9 Quality control of nanotechnology
- •5.10 Artificial intelligence in gastrointestinal endoscopy
- •5.11 Future perspectives
- •5.12 Limitations of nanotechnology
- •5.13 Conclusion
- •6.1 Introduction
- •6.2 Global burden of gastric cancer
- •6.3 Gastric cancer risk factors
- •6.3.1 Infection with Helicobacter pylori
- •6.3.2 Age and sex
- •6.3.3 Cigarette smoking
- •6.3.4 Obesity and metabolic dysfunction
- •6.3.5 Dietary factors
- •6.3.6 Alcohol use
- •6.3.7 Medications
- •6.3.8 Host genetics
- •6.4 Other risk factors
- •6.4.1 Epstein–Barr virus infection
- •6.4.2 Autoimmune disorders
- •6.4.3 Ménétrier’s disease
- •6.5 Nanotechnology in cancer diagnostic and therapeutics
- •6.6 Nanotechnology and gastric cancer diagnostic
- •6.6.1 Fluorescence imaging and gastric cancer detection
- •6.6.2 Photoacoustic imaging and gastric cancer detection
- •6.6.3 Computed tomography and gastric cancer detection
- •6.6.4 Magnetic resonance imaging and gastric cancer detection
- •6.6.5 Multimodal imaging and gastric cancer detection
- •6.7 Nanotechnology and gastric cancer management
- •6.7.1 Nanomaterial and chemotherapy
- •6.7.2 Nanomedicine and radiotherapy
- •6.7.3 Phototherapy and gastric cancer detection
- •6.7.4 Combination therapies and theranostics for gastric cancer detection
- •6.8 Challenges and prospectives
- •Acknowledgments
- •References
- •7.1 Introduction
- •7.2 Nanoparticles as drug delivery systems
- •7.2.1 Advantages of nanoparticles for drug delivery
- •7.2.2 Types of nanoparticles used in gastric cancer treatment
- •7.2.3 Targeted drug delivery to gastric cancer cells
- •7.3 Nanoparticles for imaging and diagnosis
- •7.3.1 Nanoparticles in gastric cancer imaging
- •7.3.2 Contrast agents and theranostic nanoparticles
- •7.3.3 Molecular imaging and targeting approaches
- •7.4 Therapeutic applications of nanoparticles in gastric cancer
- •7.4.1 Chemotherapy with nanoparticle formulations
- •7.4.2 Photothermal and photodynamic therapy
- •7.4.3 Immunotherapy and nanoparticles
- •7.4.4 RNA interference (RNAi) and gene therapy
- •7.5 Nanoparticles for combination therapy
- •7.5.1 Synergistic effects of nanoparticle-based combination therapies
- •7.5.2 Sequential and simultaneous delivery of therapeutics
- •7.6 Challenges and limitations of nanoparticle-based therapy
- •7.6.1 Biocompatibility and toxicity concerns
- •7.6.2 Nanoparticle clearance and stability
- •7.6.3 Regulatory aspects and clinical translation
- •7.7.1 Preclinical studies and animal models
- •7.7.2 Clinical trials and human studies
- •7.7.3 Promising results and future directions
- •7.8 Nanoparticles in personalized medicine for gastric cancer
- •7.8.1 Biomarker-driven nanoparticle therapies
- •7.8.2 Individualized treatment approaches
- •7.9 Nanoparticle-based theranostics for gastric cancer
- •7.9.1 Diagnostic and therapeutic integration
- •7.9.2 Multifunctional nanoparticle platforms
- •7.10 Future perspectives and concluding remarks
- •Acknowledgments
- •References
- •8.1 Introduction
- •8.2 Artificial intelligence role in hepatitis disease
- •8.3 Artificial intelligence role in non-alcoholic fatty liver disease
- •8.4 Artificial intelligence role in hepatocellular carcinoma
- •8.5 Conclusion
- •References
- •9.1 Introduction
- •9.2 Overview of clinical decision support
- •9.2.2 Medical imaging and diagnostic services
- •9.2.3 Virtual patient care
- •9.2.4 Patient safety
- •9.2.5 Diagnostic support
- •9.2.6 Medical research and drug discovery
- •9.2.7 Rehabilitation
- •9.2.8 Administrative applications
- •9.3 Types of AI algorithms in CDS
- •9.3.1 Machine learning algorithms
- •9.3.2 Bayesian Gaussian regression
- •9.4 Supervised learning
- •9.4.1 Diagnosis and treatment prediction
- •9.5 Unsupervised learning
- •9.6 Deep learning and neural networks
- •9.7 Natural language processing (NLP) techniques
- •9.7.1 Convolutional neural networks (CNNs) for medical image analysis
- •9.7.2 Recurrent neural networks (RNNs) for signal processing
- •9.8 Current AI-based clinical data support system
- •9.9 Challenges and considerations
- •9.9.1 Current AI-based CDS systems
- •9.10 Regulatory and ethical issues (HIPAA, GDPR, etc)
- •9.11 Challenges for clinical translation
- •References
- •9.12 Obstacles, restrictions, and missing knowledge
- •9.13 Future trends
- •9.14 Future trends and developments
- •9.14.1 Advancements in AI algorithms
- •9.15 Expansion to point-of-care devices
- •9.16 AI-driven drug discovery
- •9.17 AI in public health and epidemiology
- •9.18 Conclusion
- •10.1 Introduction
- •10.2 Developing history of AI
- •10.3 AI’s role in the early detection of GC
- •10.3.1 Screening of GC by AI
- •10.3.2 Accuracy of sampling from early endoscopic diagnosis
- •10.3.3 Digital pathological diagnosis
- •10.4 Role of AI from endoscopic diagnosis to treatment
- •10.5 Artificial intelligence in surgery
- •10.6 Molecules and genes
- •10.7 AI models’ function in prognosis prediction
- •10.7.1 Metastasis and staging prediction
- •10.7.2 AI aided treatment decisions
- •10.7.3 Clinical massive data analysis and prognostic prediction
- •10.8 Survival analysis
- •10.9 Conclusion and future prospects
- •References
- •11.1 Introduction
- •11.2 Stages of liver fibrosis
- •11.3 Etiology of liver fibrosis
- •11.3.1 Chronic viral hepatitis
- •11.3.2 Alcohol-related liver disease (ALD)
- •11.4 Pathogenesis
- •11.5 Symptoms
- •11.6 Diagnosis
- •11.7 Invasive approach
- •11.7.1 Liver biopsy
- •11.7.2 Limitations of liver biopsy
- •11.8 Non-invasive approach
- •11.8.1 Ultrasonographic based
- •11.9 Non-surgical tests
- •11.9.1 Serum biomarkers
- •11.10 Treatment
- •11.11 Limitations of antifibrotic therapy
- •11.12 Role of nanomedicines in the treatment of hepatic fibrosis
- •11.13 Type of nanoparticles currently in use for LF
- •11.13.1 Phytochemical compound for LF
- •11.13.3 siRNA derived NPs
- •11.14 HSC targeted nanoparticle delivery
- •11.15 Advantage of nanomedicine for LF
- •11.15.2 Enhanced drug delivery
- •11.15.4 Reduced adverse effects
- •11.15.5 Improved pharmacokinetic properties
- •11.16 Challenges of nm for LF
- •11.17 Future of nm in the treatment of LF
- •References
- •12.1 Introduction
- •12.2 Medical requirement for colonoscopy
- •12.3 Limitation of colonoscopy
- •12.4 Advancement of colonoscopy
- •12.5 High-definition and ultra-high-definition imaging technology
- •12.6 Computed tomography
- •12.7 Artificial intelligence and machine learning
- •12.8 Advancement in patient experience
- •12.9 Capsule endoscopy
- •12.10 Simulated detection systems
- •12.11 Improved training and workshop programs
- •12.12 Future of colonoscopy
- •12.13 Multi-spectral imaging
- •12.14 Machine learning algorithms integration
- •12.15 Robotic-assisted colonoscopy
- •12.16 Virtual colonoscopy
- •12.17 Tailoring colonoscopy screening
- •12.18 Patient-compatible techniques
- •12.19 Remote monitoring and consultations
- •12.20 Alternative bowel preparation methods
- •12.21 Preventive measures enhancement
- •12.22 Conclusion
- •References

Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
biology, followed by the formation of a customized and molecular-based method for
the administration of anticancer medications. Proper medications for cancerous
diseases rely highly on their timely diagnosis for which in vivo molecular imaging
technique is popular but a trend for a more feasible approach is seen as molecular
imaging requires specialized molecular probes. The use of nanoparticles (NPs) is the
current paradigm for diagnosing and treating gastric cancer. With the advent of
extensive explorations in the field of nanotechnology, NPs have been realized to
have proficient curative properties for gastric cancer. Since the past decade,
extensive research work has been allocated to applications of NPs in the direction
of therapeutics and diagnosis. Several reports have documented that NPs-based
therapeutic agents overcome problems associated with conventional therapy. But, it
seems that perusal of the characteristics of NPs and their interactive efficacies with
biological entities is vital to analyze the potential of NPs-based nanomedicines and
NPs-based diagnostic protocols. Now-a-days green synthesized NPs are also used as
a potential agent for gastric cancer treatment. This study is significant since NPs
might also pose certain side effects and toxicity and these aspects should be well
addressed prior to the utilization of NPs in biological systems. This chapter will
encompass the diverse purview of NPs and how this can be a plausible alternative in
the diagnosis and therapeutic treatment of gastric cancer.
Chapter 8
Artificial intelligence in hepatitis and chronic liver disease
Akbar Hamid
1
Department of Gastroenterology, Sanjay Gandhi Postgraduate Institute of
Medical Sciences, Lucknow, Uttar Pradesh 226014, India
2
Department of Pharmacology, Heritage Institute of Medical Sciences (HIMS),
Varanasi-221311, India
3
Department of Gastroenterology, Institute of Medical Sciences, Banaras Hindu
1
, Gira Sulabh2and Vinod Kumar3*
University, Varanasi, Uttar Pradesh 221005, India
*Corresponding author (vinodkumarchief@gmail.com)
Artificial Intelligence (AI) is a well-developing field of computer science that
imitates human technical thinking to solve problems. The use of different AI models
in hepatology is a recent development in the medical field for better diagnostics.
Conventional diagnostic methods are being integrated with modern AI to enhance
the performance of treatment. AI’s ability to miming the data in human parameters,
and forecast the occurrence of hepatitis and other chronic liver diseases. Classifying
the different stages of hepatitis, fatty liver disease and hemochromatosis are possible
along with the diagnosis and screening. Early disease prediction, complications and
mortality can be studied using the algorithms such as regression models, since
hepatitis early diagnosis is clinically limited in early stages. AI can predict the risk
related to the vascular invasion of hepatocellular carcinoma and hepatitis related to
cirrhosis. It also calculates the liver failure rate in HCC patients. Ultimately AI will
eventually help in reducing medical errors and managing the patient clinical output.
xxx

Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
Chapter 9
Artificial intelligence applications for clinical decisions support
Bhaskar Sharma
Chaturvedi
1
Neurobiology Laboratory, Department of Anatomy, All India Institute of
Medical Sciences, New Delhi 110029, India
2
Systems Toxicology Group, CSIR-Indian Institute of Toxicology Research
3
1*
, Renu Negi2, Anjali Yadav1, Yogesh Sharma1and Vivek K
Vishvigyan Bhavan, 31, Mahatma Gandhi Marg, Lucknow, Academy of Scientific
and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh 201002, India
3
Department of Gastroenterology, Institute of Medical Sciences, Banaras Hindu
University, Varanasi, India
*Corresponding author (sharma.bhaskar003@gmail.com)
Clinical Decision Support (CDS) systems represent a groundbreaking advancement in healthcare, fundamentally changing how clinicians make critical decisions
by offering evidence-based guidance and knowledge directly at the point of care. By
seamlessly integrating with electronic health record (EHR) systems, these platforms
harness extensive patient data, medical literature, and best practice guidelines,
empowering clinicians with the insights needed for informed decision-making.
Through sophisticated analysis of large datasets, CDS systems uncover nuanced
patterns and insights that enable early intervention and optimize resource allocation,
thereby enhancing patient care outcomes. Despite the transformative potential of
CDS, concerns persist regarding algorithm bias, data privacy, and stakeholder
engagement, necessitating careful consideration and ongoing refinement. Case
studies underscore the tangible impact of CDS, demonstrating its ability to enhance
adherence to clinical standards, reduce hospital readmissions, and elevate patient
satisfaction levels. Furthermore, the integration of artificial intelligence (AI)
technologies bolsters the capabilities of CDS systems across various domains,
including medical imaging analysis, virtual patient care, medication safety assurance, diagnostic support, medical research facilitation, and rehabilitation.
Administrative applications of AI within CDS systems streamline essential tasks
such as claims processing and clinical documentation, driving operational efficiency
and alleviating administrative burdens on healthcare professionals. In summary,
CDS systems play a pivotal role in revolutionizing healthcare delivery by equipping
clinicians with actionable insights, improving clinical decision-making, and ultimately leading to better patient outcomes.
Chapter 10
Role of artificial intelligence in an early diagnosis and prediction of gastric cancer as
an advanced therapeutic technique
Juhi Singh
1
Department of Gastroenterology, Institute of Medical Sciences, Banaras Hindu
1
and Vinod Kumar Dixit
1*
University, Varanasi 221005, India
*Corresponding author (drvkdixit@gmail.com, vkdixit@bhu.ac.in) (Phone number:
8601100564)
xxxi

Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
One of the most prevalent malignant tumours with a high fatality rate is gastric
cancer (GC). Human professionals’ meticulous assessments of medical pictures are
crucial for making accurate diagnoses and treatment choices for GC. This ailment
has historically proven difficult to diagnose. Furthermore, the imaging settings,
limited expertise, objective criteria, and inter-observer inconsistencies impede the
development of accuracy. Healthcare research has advanced thanks to artificial
intelligence (AI). Applications that help with cancer diagnosis and prognosis have
been developed as a result of the accessibility of open-source healthcare statistics.
Accurate evaluation, diagnosis, and treatment of stomach malignant growth and
helicobacter pylori bacteria can be achieved with AI-assisted image analysis; links
between these sub-fields can give more information than traditional analysis. AIassisted categorization of genomic, epigenetic, and metagenomic data may lead to
improved personalised therapy recommendations for gastrointestinal malignancies.
In a number of therapeutic settings, including GC, researchers are looking at the
extensive uses of artificial intelligence (AI). With endoscopic inspection and
pathologic evidence during GC screening, AI can identify precancerous conditions
and help with early cancer identification. AI can help TNM staging and subtype
categorization in the diagnosis of GC. AI can assist with prognosis prediction and
surgical margin estimation for treatment options. Here, we include some AI
methods for early stomach cancer prediction. Even though several methods
advocated in various texts have shown excellent prediction outcomes, cancer
mortality has not decreased. As a result, a further in-depth study is needed in the
field of cancer prediction in relation to AI that may be applied as a therapy.
Chapter 11
Nanomedicines in liver fibrosis
Saras Tiwari
Vivek K Chaturvedi
1
Department of Cellular and Molecular Medicine, Faculty of Medicine,
University of Ottawa, Canada
2
Neurobiology Laboratory, Department of Anatomy, All India Institute of
Medical Sciences, New Delhi 110029, India
3
State University of New York Upstate Medical University, USA
4
Electron Microscopy Facility, All India Institute of Medical Sciences, New
Delhi 110029, India
5
Department of Gastroenterology, Institute of Medical Sciences, Banaras Hindu
1
, Bhaskar Sharma2, Jugasmita Deka3, Prabhakar Singh4*and
5
University, Varanasi, India
*Corresponding author (prabhakar.singh@aiims.edu)
Chronic infection of liver cells causes scarring on liver tissue, resulting in Liver
Fibrosis (LF), which is now a major global health concern. Hepatitis C, Hepatitis B,
and alcohol abuse are the leading causes of liver damage, which results in the
deposition of Extracellular cell matrix (ECM) and liver fibrosis. Ultrasonography
and magnetic resonance imaging are commonly used as non-invasive diagnostic
methods for hepatic fibrosis. The conventional therapy used to treat liver diseases is
xxxii

Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
ineffective because it does not deliver a sufficient amount of drug concentration in the
liver and is imprecise. Several clinical and preclinical Study has shown that the
utilisation of nanotechnology to deliver therapeutic agents including drug molecules,
and nucleic acids, in adequate amount and to target specifically the HSC (hepatic
stellate cells) could be the future treatment to cure Liver diseases caused by LF.
According to research, nanomedicines can reverse premature hepatic fibrosis. Many
nanoparticulate systems (NPs) such as Liposomes, Inorganic NPs, and Nano-micelles
have been studied because of their diverse properties for drug delivery and in addition
to some therapeutic moieties. Out of these, Liposomal NPs have shown very promising
results in clinical trials and are being considered as an extremity for the treatment of
hepatic fibrosis. This book chapter discusses the causes, pathogenesis, diagnosis, and
nanoparticulate systems used in the treatment of chronic liver diseases.
Chapter 12
Artificial intelligence (AI) based colonoscopy
Akbar Hamid
Gira Sulabh
1
Department of Hepatology, Sanjay Gandhi Post Graduate Institute of Medical
Sciences, Lucknow, India
2
Maharaja Agrasen School of Pharmacy, Maharaja Agrasen University, Atal
Shiksha Kunj, Solan, Himachal Pradesh, India
3
Department of Pharmacology, Heritage Institute of Medical Sciences (HIMS),
Varanasi-221311, India
4
Department of Gastroenterology, Institute of Medical Sciences, Banaras Hindu
1#
3
, Vinod Kumar4* and D P Yadav4*
, Rajesh Kumar2#, Vivek K Chaturvedi3, Sunil Dutt2,
University, Varanasi, India
#Sharing co-first author
*Corresponding author (vinodkumarchief@gmail.com; devesh.thedoc@gmail.com)
With the increase in the world population and development, a number of healthrelated issues are also increasing in gastrology. One of the major causes is poor food
habits. To deal with this constant advancement is required in the field of medical
sector which will not only help in easy and earlier diagnosis of the underlying health
issue but also in accurate diagnosis. In this chapter advancement and collaboration
of artificial intelligence with the medical sector are discussed below. How one
technique helps is the accurate detection of colorectal cancer as well as other disease
such as IBD or any other abnormalities in the colon. A different version of
colonoscopy has been developed along with artificial intelligence discussed in this
chapter with the future aspect and advancement.
xxxiii

List of contributors
Zeba Azim
Department of Botany, University of Allahabad, Prayagraj 211002, India
Bharmjeet
Department of Biotechnology, Delhi Technological University, New Delhi
110042, India
Prakash Chandra
Department of Biotechnology, Delhi Technological University, New Delhi
110042, India
Vivek K Chaturvedi
Department of Gastroenterology, Institute of Medical Sciences, Banaras Hindu
University, Varanasi, Uttar Pradesh 221005, India
Asmita Das
Department of Biotechnology, Delhi Technological University, New Delhi
110042, India
Jugasmita Deka
State University of New York Upstate Medical University, USA
Vinod Kumar Dixit
Department of Gastroenterology, Institute of Medical Sciences, Banaras Hindu
University, Varanasi 221005, India
Sunil Dutt
Maharaja Agrasen School of Pharmacy, Maharaja Agrasen University, Atal
Shiksha Kunj, Solan, Himachal Pradesh 174103, India
Niraj Kumar Goswami
Mahant Avaidyanath Government Degree College, Jungle Kaudia, Gorakhpur,
India
Ankush Goyal
Maharaja Agrasen School of Pharmacy, Maharaja Agrasen University, Atal
Shiksha Kunj, Solan, Himachal Pradesh 174103, India
Rahul Gupta
Department of Information Technology, Delhi Technological University, New
Delhi 110042, India
Akbar Hamid
Department of Hepatology, Sanjay Gandhi Postgraduate Institute of Medical
Sciences, Lucknow, Uttar Pradesh 226014, India
xxxiv

Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
Mazharul Haque
School of Biological Sciences, CNBR, Alabama State University, USA
Ashutosh Kumar
Department of Ophthalmology, University of California Los Angeles, California-
90095, USA
Brijesh Kumar
Department of Pharmacology, Institute of Medical Sciences, Banaras Hindu
University, Varanasi 221005, India
Rajesh Kumar
Maharaja Agrasen School of Pharmacy, Maharaja Agrasen University, Atal
Shiksha Kunj, Solan, Himachal Pradesh 174103, India
Vinod Kumar
Department of Gastroenterology, Institute of Medical Sciences, Banaras Hindu
University, Varanasi, Uttar Pradesh 221005, India
Renu Negi
Systems Toxicology Group, CSIR-Indian Institute of Toxicology Research
Vishvigyan Bhavan, 31, Mahatma Gandhi Marg, Lucknow, Academy of
Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh 201002,
India
Nida-e-Falak
Department of Biotechnology, Delhi Technological University, New Delhi
110042, India
Ritu
Department of Biotechnology, Delhi Technological University, New Delhi
110042, India
Bhaskar Sharma
Neurobiology Laboratory, Department of Anatomy, All India Institute of
Medical Sciences, New Delhi 110029, India
Yogesh Sharma
Neurobiology Laboratory, Department of Anatomy, All India Institute of
Medical Sciences, New Delhi 110029, India
Anshu Singh
Department of Chemistry, Institute of Science, Banaras Hindu University,
Varanasi 221005, India
Anurag K Singh
Department of Pharmaceutical Engineering and Technology-Indian Institute of
Technology, BHU, Varanasi, India
xxxv

Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
Cancer Biology Research and Training, Department of Biological Sciences,
Alabama State University, 915 S Jackson Street, Montgomery AL 361010271,
USA
Jay Singh
Department of Chemistry, Institute of Science, Banaras Hindu University,
Varanasi 221005, India
Juhi Singh
Department of Gastroenterology, Institute of Medical Sciences, Banaras Hindu
University, Varanasi 221005, India
Kshitij R B Singh
Graduate School of Life Science and Systems Engineering, Kyushu Institute of
Technology, Kitakyushu, Japan
Prabhakar Singh
Electron Microscopy Facility, All India Institute of Medical Sciences, New Delhi
110029, India
Pratistha Singh
Department of Ophthalmology, University of California Los Angeles, California
90095, USA
Shefali Singh
Department of Botany, Kashi Naresh Government Post Graduate, College,
Gyanpur, Bhadohi, U.P. 221304, India
Gira Sulabh
Department of Pharmacology, Heritage Institute of Medical Sciences (HIMS),
Varanasi-221311, India
Saras Tiwari
Department of Cellular and Molecular Medicine, Faculty of Medicine,
University of Ottawa, Canada
Komal Vig
School of Biological Sciences, CNBR, Alabama State University, USA
Anjali Yadav
Neurobiology Laboratory, Department of Anatomy, All India Institute of
Medical Sciences, New Delhi 110029, India
Dawesh P Yadav
Department of Gastroenterology, Institute of Medical Sciences, Banaras Hindu
University, Varanasi 221005, India
Navneet Yadav
Department of Mechanical Engineering, Faculty of Science and Engineering,
Swansea University, Swansea SA1 8EN, United Kingdom
xxxvi

Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
Ravi Kumar Yadav
Department of Botany, Kashi Naresh Government Post Graduate, College,
Gyanpur, Bhadohi, U.P. 221304, India
Mohammad Zafaryab
School of Biological Sciences, CNBR, Alabama State University, USA
xxxvii

Introduction
Nanotechnology and artificial intelligence (AI) have the potential to transform the
existing treatment and diagnosis choices for gastrointestinal (GI) disorders. Several
studies have shown that GI diseases can be early diagnosed and successfully treated
using nanomaterials associated with AI applications. The GI tract has become a
considerable target system for nanotechnology and AI, and it contains a wide range
of substances, such as water, nutrients, or therapeutics that are absorbed in the GI
tract when transported through the digestive tract. The behaviour of nanotechnology employed for GI disease diagnosis or therapy can be controlled depending on
the pH, pressure, transit duration, and bacterial concentration of each specific
nanomaterial. Because of their adjustable interactions with macrophages, M cells,
immune cells and intestinal epithelial cells nanoparticles have demonstrated considerable promise in gastroenterology and may become a potential delivery system
for vaccines. The use of AI-based advanced machines for GI surgery as well as in the
study of medicine is expanding quickly. AI within the diagnostic process supports
medical specialists to improve the level of diagnostic accuracy and efficiency, thus
providing emergent digitalized healthcare services. Nanotechnology with AI is
anticipated to have a significant impact on how GI disorders are diagnosed and
treated. In terms of effectiveness, dependability, and practicality, several of the
medicines and diagnostics based on AI described here outperform traditional
materials. In the future, GI problems may be successfully treated using AI-based
machines and their intricate mixes, which may include therapeutic substances. This
book explains how the most recent advances in applications of novel biomaterials,
nanotechnology and AI have paved the way for breakthroughs in drug delivery. This
book demonstrates present and future applications in a setting where it is essential to
provide effective, patient-centered, and long-lasting healthcare systems. This book
provides an overview of the technological approaches mainly focused on the role of
AI and their implications in GI disorders such as abdominal pain, bowel obstruction, diarrhoea, pancreatitis, upper gastrointestinal bleeding (UGIB), non-alcoholic
fatty liver diseases (NAFLD), intestinal tuberculosis (ITB), celiac disease, and
duodenal ulcer as well as include the role of nanotechnology and AI in GI illnesses.
Due to its high calibre material, the book will appeal to a wide range of readers,
including academics, students, researchers and medical students as well as practitioners. It would be particularly interesting to readers interested in health, business,
and research linked to the biomedical sciences. The main marketing and differentiating factors are the numerous libraries operating in numerous reputable private
and governmental institutions or organizations.
xxxviii

IOP Publishing
Nanobiotechnology and Artificial Intelligence in
Gastrointestinal Diseases
Vivek K Chaturvedi, Anurag Kumar Singh, Jay Singh and Dawesh P Yadav
Chapter 1
Nanotechnology and artificial intelligence
Anshu Singh, Vivek K Chaturvedi, Anurag K Singh, Jay Singh, Kshitij R B Singh and
Dawesh P Yadav
The convergence of nanotechnology and artifi cial intelligence (AI) in medical
science heralds a transformative era, promising groundbreaking innovations in
diagnostics, therapeutics, and personalized medicine. Nanotechnology, operating at
the scale of individual atoms and molecules, facilitates the design of advanced
materials with unique properties, enabling precise drug delivery, diagnostic imaging,
and theranostics. On the other hand, AI, with its prowess in machine learning (ML)
and data analysis, enhances medical decision-making, diagnostic accuracy, and
patient care. This chapter explores the revolutionary synergy between nanotechnology and AI, examining their individual contributions and the synergistic effects
when integrated. In the realm of nanotechnology, the utilization of nanomaterials
for drug delivery systems is explored, showcasing their ability to enhance targeting,
reduce side effects, and revolutionize treatment, with a particular focus on successful
applications in cancer therapy. Additionally, the development of nanosensors for
diagnostics is discussed, emphasizing their role in early disease detection, real-time
monitoring, and imaging.
1.1 Introduction
The convergence of nanotechnology and AI in the field of medical science marks a
paradigm shift that holds the promise of revolutionizing healthcare on an unprecedented scale. This groundbreaking synergy combines the precision and versatility of
nanoscale technologies with the analytical prowess of intelligent algorithms, paving
the way for transformative advancements in diagnostics, treatment modalities, and
overall patient care. Nanotechnology, operating at the scale of individual atoms and
molecules, allows for the precise engineering of materials and devices with novel
properties. This capability has given rise to a myriad of applications, ranging from
doi:10.1088/978-0-7503-6134-7ch1 1-1 ª IOP Publishing Ltd 2024
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