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Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
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IOP Publishing
Nanobiotechnology and Artificial Intelligence in
Gastrointestinal Diseases
Vivek K Chaturvedi, Anurag Kumar Singh, Jay Singh and Dawesh P Yadav
Chapter 10
Role of artificial intelligence in an early
diagnosis and prediction of gastric cancer as an
advanced therapeutic technique
Juhi Singh and Vinod Kumar Dixit
One of the most prevalent malignant tumors 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 subfields can give more information than traditional analysis.
AI-assisted categorization of genomic, epigenetic, and metagenomic data may
lead to improved personalized therapy recommendations for gastrointestinal malignancies. In a number of therapeutic settings, including GC, researchers are looking
at the extensive uses of AI. With endoscopic inspection and pathologic evidence
during GC screening, AI can identify precancerous conditions and help with early
cancer identification. AI can help tumor, nodes, and metastases (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, further in-depth study is needed in the
field of cancer prediction in relation to AI that may be applied as a therapy.
doi:10.1088/978-0-7503-6134-7ch10 10-1 ª IOP Publishing Ltd 2024
Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
10.1 Introduction
GC is the fifth most common malignant tum or and the fourth le ading cause of
cancer-related death. In 2020, over one million fresh cases of cancer and 769 000
casualties (that is in every 13 patients 1 death) were reported. Men experience
mortality and incidence rates twice as high as women do, with Eastern Asia having
the highest rates overall. Advanced stomach cancer has a terrible prognosis, with a
less than 30% 5-year survival rate. However, early stomach cancer can have a 90%
chance of survival, but because of its vague symptoms, it is difficult to find [1, 2].
The most frequent p rocedure for early detection is endoscopic inspection, and a
biopsy is required for a conclusive diagnosis [3]. The subtypes and stages of the
tumor can be identified using pathology and computed tomography (CT) imaging,
which can be used to guide treatment choices and forecast prognoses. Radical
resection is recommended for patients with the initial stages of stomach cancer,
whereas advanced cases may necessitate a triage approach that includes surgery,
chemotherapy, and radiotherapy [4, 5]. Excellent prognoses for particular forms of
stomach cancer have been demonstrated by immunotherapy and molecularly
targeted medications [6]. In the realm of stomach cancer, AI technology h as
been extensively used for image analysis, prognosis, and diagnosis. Limited
experience, objective standards, and inter-observer differences can all be addressed
by AI [7]. Traditional ML methods rely on handcrafted features, while deep
learning (DL) has achieved great success in medical image processing. DL models
are currently effectively used in medical image processing using massive datasets
and better methods. This chapter aims to contribute a comprehensive overview of
AI, its condition and role in diagnosis, and recommendations for future research in
related domains to medical professionals engaged in the detection of stomach
cancer [8–10].
In recent years, there has been an abundance of biomedical data available in the
medical field, leading to the emergence of the big data era [11]. Physicians now face
the challenge of effectively analyzing this data rather than just collecting it. AI refers
to a machine’s ability to learn and display intelligence [12]. In the age of personalized
medicine, AI can assist in more effectively converting massive data into useful
insights, minimizing errors, enhancing diagnostic precision, offering real-time
forecasts, and even providing advice after discharge. Cancer management is being
revolutionized and reshaped by AI, which has seen increased application in recent
years. Interpreting images is one example of how AI is used to manage cancer [13],
surgical interventions [14 ], drug discovery, surgical skills training and assessment
[15], hospital-wide data analysis [16], and personalized treatment [17]. AI is largely
utilized for prognosis prediction, therapy advice, and early identification of stomach
cancer. The methodical investigation of AI-assisted techniques is covered in this
chapter, along with AI’s potential drawbacks and potential future applications.
Based on four factors, we have presented the state of AI in GC in this review:
(1) Clinical big data analysis and prognosis prediction; (2) precise sampling from
early diagnosis (endoscopy); (3) digital pathological diagnosis; (4) molecules and
genes (figure 10.1).
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Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
Figure 10.1. The alignment diagram of this chapter.
10.2 Developing history of AI
In stomach cancer research, treatment and prevention has increasingly relied on
advanced technologies, including AI. The development of AI has revolutionized the
way that stomach cancer is treated, as it allows for advanced screening, diagnosis,
and prognosis prediction. AI, on the other hand, refers to the intelligence exhibited
by machines. The term ‘cognitive machines,’ which was first used in 1956, describes
devices or computers that mimic human cognitive processes like learning and
problem-solving [18]. Machine learning (ML) is a subset of AI that utilizes computer
algorithms to improve through experience [19]. Radiology, neurology, orthopedics,
pathology, ophthalmology, and gastroenterology are just a few of the medical
specialties where ML methods like random forest, support vector machines (SVM),
and artificial neural networks (ANNs) have been used to develop models based on
training data. In many ongoing projects as of 2020, DL has taken the lead. In order
to gradually extract higher-level features from the initial input, it employs many
layers. In a nutshell, DL is used to implement (ML, which is an important field of
AI. Several AI models have emerged in the field of stomach cancer thanks to recent
improvements in hardware and computational capability [20–27]. While studies
concentrated on recurrence, metastasis, and forecasting survival for prognosis
[32–34], the usage of AI-assisted diagnostics primarily comprises blood reports,
medical imaging such as computed tomography (CT) and endoscopy [26–29]. The
use of AI in medicine has been eagerly investigated, and DL technology has quickly
attracted attention as the best ML technique. DL has been utilized extensively in
medicine [25–27], particularly for tumors including skin [28], breast [29], and
stomach [30, 31] cancers.
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Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
10.3 AI’s role in the early detection of GC
Due to the vague and generic signs of GC, it is sometimes not discovered until it has
progressed to an advanced stage, which has a bad prognosis. However, the five-year
survival rate for stomach cancer can be greatly raised by up to 90% with early and
correct identification.[35, 36] However, the capacity to diagnose early stomach
cancer is constrained by the availability of skilled imaging specialists, and diagnostic
efficacy greatly depends on their clinical background. All false diagnoses and missed
diagnoses happen, even to the most qualified professionals. AI techniques are able to
process and analyze vast volumes of data, mimicking human cognitive function, and
help gastroenterologists make diagnoses and decisions. Endoscopy, pathology, and
CT imaging are just a few of the medical imaging domains where AI has already
been put to use. Extraction of picture features [37, 38], early detection of stomach
cancer [39–43], early diagnosis of precancerous conditions [40], narrow-band
imaging for magnifying endoscopic optimization, and use of Raman endoscopy
are all steps in AI-assisted endoscopic diagnosis[44, 45]. Automatic GC identification [46], detection of GC using whole slide imaging (WSI) [47–50], automatic
identification of tumor-infiltrating lymphocytes (TILs) [51], and segmentation of
lesion areas [52–54] are all components of AI-assisted pathologic diagnosis.
Preoperative peritoneal metastasis detection [54], perigastric metastatic lymph
node detection [55], and the utilization of two more innovative imaging approaches
[56] are the main goals of AI-assisted CT diagnosis. The diagnostic performance of
these AI models is on a level with human experts in some situations. Detecting GC
and precancerous lesions early on is crucial for improving survival rates. Although
endoscopy is widely used for GC screenings, diagnosing early gastric cancer (EGC)
through image analysis can be challenging and subjective due to cognitive and
technical factors. Fortunately, there are effective methods to improve diagnostic
accuracy such as use of image enhanced endoscopy, as well as narrow-band imaging
(NBI) and blue-laser imaging (BLI), which are more efficient than traditional white
light imaging. To increase diagnosis accuracy and prevent pointless biopsies, AIassisted evaluation enables a more objective evaluation strategy. Recently, the
detection of EGC has been the topic of numerous investigations.
Convolutional neural network (CNN) algorithms have been found to reliably
detect EGC in pictures taken using standard and m agnifying endoscopy in recent
research [13]. The ability of this technique to distinguish EGC from normal tissue
or gastritis in real-time utilizing video images has been demonstrated to be
extremely successful [57, 58]. Results have revealed that CNN systems perform
similarly to expert systems and outperform non-expert systems in accurately
identifying EGC. Additionally, the use of AI significantly speeds up the detection
process compared to endoscopists [60 ]. Deep convolutional neural networks
(DCNNs) have been used in a unique system created by Wu et al that can identify
EGC and stomach regions without blind spots [61]. Various devic es can coop erate
in real time to make sure the endoscope can see the whole gastric mucosa, which is
necessary to detect early neoplastic changes. A trial with randomized controlled
experiment including 324 patients compared the ‘WISENSE system—which st and s
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Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
for smart and sense—to traditional endoscopy’. When compared to the controls,
WISENSE dramatically reduced the percentage of blind spots (5.9% versus 22.4%,
p 0.001) [57]. One more tool is developed that involved 1 million images of
endoscopy of more than 80 000 patients. It is called ‘GRAIDS’ (Gastrointestinal
Artificial Intelligence Diagnostic System). With accuracy equivalent to professional endoscopists and superior to non-expert endoscopists, the device may identify
upper GI cancer in real time [59]. It is possible to determine the level of invasion
using a computer-aided detection system based on CNNs. According to research,
this approach is more precise and exacting than those carried out by skilled
endoscopists. ‘ENDOANGEL’ which is an AI system that provides real-time
information and can also carry out a number of f unctions in EGC diagnosis, such
as white light endoscopy detection, enlarging narrow-band imaging, and predicting invasion depth. Although its sensitivity and negative predictive value were only
slightly higher, ENDOANGEL’sspecificity, accuracy, and positive predictive
value (93.22%, 91%, and 90%, respectively) were noticeably superior to those of
endoscopists (72.33%, 76.19%, and 70.56%, respectively). These studies have
contributed to the development of AI for clinical use, despite some limitations.
For example, validation with unaltered images and videos is necessary for accurate
results. With further improvements, the performance of AI in clinical settings is
expected to improve [61].
10.3.1 Screening of GC by AI
The research into applying AI to detect gastric carcinoma (GC) is both highly
anticipated and well-liked. Atrophic gastritis (AG), which is caused by H. pylori
(HP), is the first step in the development of GC. This is followed by gastric intestinal
metaplasia (GIM), dysplasia, and eventually malignancy [62, 63]. To reduce the
incidence of GC, it is important to identify these precancerous gastric diseases and
screen high-risk individuals [64]. Unfortunately, due to modest morphological
alterations, GC is frequently detected in an advanced stage, leading to a five-year
survival probability of only 30%. A substantially greater survival percentage of
91.5% can be achieved, however, for those who receive a diagnosis at an early stage
[65]. Therefore, early detection of GC is crucial. The common method for GC
screening is through endoscopic examination, which unfortunately has reported miss
rates ranging from 4.6% to 25.8% by endoscopists in previous studies [65–68].
Improved techniques in endoscopy with enhanced image capabilities can potentially
aid in detecting GC [69], but their widespread use is limited by the need for
specialized training and expertise. Visually analyzing ‘whole slide imaging’ (WSI),
medical images achieve after biopsy or resection is crucial for the accurate diagnosis
of GC [70]. Nevertheless, pathologists must concentrate for extended periods of time
and carry out a lot of work to find stomach cancer because of the size variations in
malignant regions and the enormous scale of WSI. To solve these problems, AI
might offer automated, accurate, and quick histo-pathological analysis and endoscopic detection. Even endoscopic pictures have been used to try to detect the
presence of H. pylori
infection [71–75].
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Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
Yan et al [72] talked about a three-category categorization method that
incorporates the eliminated state in their investigation. An accuracy rate of above
0.8 was attained by all researchers, which is comparable to that of experienced
endoscopists. In comparison to predicting HP infection, researchers found that
detecting AG and GIM had a greater accuracy prevalence of 0.9 [76–78]. However,
they struggled to accurately identify a variety of precancerous conditions and
stomach neoplasms [79–81]. Researchers investigated conventional ML techniques
[82, 83] and used DL models [84, 86, 89] to identify GC in endoscopic pictures. To
increase detection precision, they additionally used self-designed network topologies
[87, 88] and sophisticated image-enhanced endoscopy [43 , 49]. Researchers have also
concentrated on finding GC in disease pictures [89].
10.3.2 Accuracy of sampling from early endoscopic diagnosis
To accomplish endoscopic diagnosis of stomach cancer, magnifying endoscopy is
usually used in combination with narrow spectrum imaging technologies such as
narrow-band imaging [61], flexible spectral imaging colour enhancement, and BLI.
However, this method requires well-trained medical professionals to perform the
diagnostic examinations [63–65]. Unfortunately, endoscopy may miss roughly 10%
of cases of upper gastrointestinal tract cancer, particularly GC [65]. Researchers are
looking at using AI to help in the detection of stomach cancer during endoscopy to
address this problem. The aim is to reduce the instances of missed diagnoses caused
by inexperience or fatigue among endoscopic doctors. CNN, a widely used AI
model, has demonstrated efficacy in identifying malignant and non-cancerous areas
during endoscopy. These AI techniques are as accurate as or more accurate than
skilled endoscopists, with an accuracy range of 86%–92.5% [67]. This shows that
using AI approaches to aid in decision-making can be quite helpful. The rate at
which detection is achieved is on the same level with that of the most expert
endoscopists because of the great sensitivity of AI approaches, which may reach
100% [69]. SVM is a further AI model that is frequently utilized in the detection of
stomach cancer. Images from a magnifying endoscopy might be used by a system
based on SVM analysis to quantitatively detect stomach cancer. In comparison to
other regions, the tumor region’s SVM output value was noticeably different [71].
Endoscopists used a computer-aided diagnostic (CAD) system based on SVM to
diagnose early GC with a diagnostic accuracy of 96.3%, a positive prognostic value
of 98.3%, a precision of 96.7%, and a level of specificity of 95% [71]. AI can be useful
for both detection and characterization when using endoscopic pictures to diagnose
stomach cancer. The computer-aided pattern recognition system [72] and the CNN
computer-aided detection (CNN-CAD) system [73] were used to determine the
depth of wall invasion of GC.
10.3.3 Digital pathological diagnosis
Digital versions of the glass slides used for pathological investigation are known as
WSIs. For tumor classifi
cation [46] depth of invasion discrimination [90] micro-
satellite instability prediction, and minimizing the lack of sufficient well-annotated
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Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
training data [91], stomach cancer has been studied using AI approaches, such as
DL-based neural networks. However, further improvements are necessary. WSI, a
virtual equivalent of glass slides, is comparable to optical microscopy in diagnosing
GCs. AI applications in pathological diagnosis emerged with advances in WSI. By
providing two deep CNN-based techniques, Leon et al [47] evaluated the use of deep
CNN in the automated identification of stomach cancer pathological pictures. Using
all of the photos, one did morphological feature analysis, while the other separately
looked into the local distinctive features. According to Sharma et al [46] the CNN
architecture could accurately classify cancer with an accuracy of 0.6990 and detect
necrosis with an accuracy of 0.8144 in pathological image analysis. According to the
experiment results, the proposed model demonstrated excellent performance in
detecting GCs with an average accuracy of 89.72%. In order to differentiate between
stomach cancer, adenoma, and non-neoplastic tissue, Iizuka et al [48] used CNNs
and recurrent neural networks. However, the automatic segmentation of lesion
zones proved an issue in the AI-assisted pathological identification of stomach
malignancy. To address the absence of thoroughly annotated pathological imaging
data, Liang et al [28] proposed a new neural network architecture and approach
called overlapping area prediction. The DL approach was used for the first time to
segment disease pictures in order to find stomach tumors. The model achieved an
intersection over union coefficient (IOU) of 88.3% and 91.1% accuracy, which went
above what was expected for supervised learning. Qu et al [91] developed a novel
intermediate dataset and a stepwise fine-tuning-based strategy to improve the
classification performance of deep neural networks.
The efficiency of the suggested DL model for medical picture segmentation was
proved by Sun et al [92] with a mean accuracy of 91.60% and a mean IoU of 82.65%.
The Mask R-CNN model is a useful tool for medical picture segmentation,
according to different research [93]. In the field of genetic pathology, DL data
interpretation has the potential to yield valuable insights into understanding and
treating stomach cancer: the importance of genes, biomarkers, and their interpretation [93]. Liang et al identified certain genes and their functions in carcinogenesis
by analyzing numerous transcription datasets and tabulating genomic data from
stomach cancer patients and healthy persons [51]. Datasets were analyzed and
ranked using Rank Prod and INMEX. Gene expression data was obtained from the
Gene Expression Omnibus database and combined with literature analysis and
bioinformatics data to identify promising genes to increase comprehension, Geno
Ontology and route analysis were employed. Progastricism (PGC) and collagen type
VI alpha 3 chains (COL6A3) were two of the 1153 differentially expressed genes that
remained after elimination, which can serve as biomarkers for GC [79]. AI-assisted
applications have enormous potential benefits for detecting GC and improving
image segmentation efficiency and diagnostic time.
AI analysis is utilized in the area of digital pathology to identify cancer, segment it,
classify mutations, forecast clinical outcomes, and discover new drugs. The unification
of pathology and oncology is becoming more crucial with the emergence of precision
oncology. Limiting radiation exposure and performing numerous computations
have advantages, but AI can also aid patients and medical staff [63
, 93].
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