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World Journal of
Gastroenterology
ISSN 1007-9327 (print)
ISSN 2219-2840 (online)
World J Gastroenterol 2021 July 7; 27(25): 3693-3950
Published by Baishideng Publishing Group Inc
WJG https://www.wjgnet.com IJuly 7, 2021 Volume 27 Issue 25
World Journal of
Gastroenterology
W J G
Contents Weekly Volume 27 Number 25 July 7, 2021
OPINION REVIEW
Approach to medical therapy in perianal Crohn’s disease
3693
Vasudevan A, Bruining DH, Loftus EV Jr, Faubion W, Ehman EC, Raffals L
REVIEW
Incorporating mucosal-associated invariant T cells into the pathogenesis of chronic liver disease
3705
Czaja AJ
Artificial intelligence in small intestinal diseases: Application and prospects
3734
Yang Y, Li YX, Yao RQ, Du XH, Ren C
Impact of the COVID-19 pandemic on inflammatory bowel disease patients: A review of the current
evidence
3748
Kumric M, Ticinovic Kurir T, Martinovic D, Zivkovic PM, Bozic J
Management of hepatitis B virus infection in patients with inflammatory bowel disease under
immunosuppressive treatment
3762
Axiaris G, Zampeli E, Michopoulos S, Bamias G
MINIREVIEWS
Worldwide management of hepatocellular carcinoma during the COVID-19 pandemic
3780
Inchingolo R, Acquafredda F, Tedeschi M, Laera L, Surico G, Surgo A, Fiorentino A, Spiliopoulos S, de’Angelis N, Memeo
R
Human immune repertoire in hepatitis B virus infection
3790
Zhan Q, Xu JH, Yu YY, Lo KK E, Felicianna, El-Nezami H, Zeng Z
Emerging applications of radiomics in rectal cancer: State of the art and future perspectives
3802
Hou M, Sun JH
Advances in paediatric nonalcoholic fatty liver disease: Role of lipidomics
3815
Di Sessa A, Riccio S, Pirozzi E, Verde M, Passaro AP, Umano GR, Guarino S, Miraglia del Giudice E, Marzuillo P
Autoimmune pancreatitis and pancreatic cancer: Epidemiological aspects and immunological
considerations
3825
Poddighe D
Gut microbiota in obesity
3837
Liu BN, Liu XT, Liang ZH, Wang JH
WJG https://www.wjgnet.com II July 7, 2021 Volume 27 Issue 25
World Journal of Gastroenterology
Contents Weekly Volume 27 Number 25 July 7, 2021
ORIGINAL ARTICLE
Basic Study
Zinc oxide nanoparticles reduce the chemoresistance of gastric cancer by inhibiting autophagy
3851
Miao YH, Mao LP, Cai XJ, Mo XY, Zhu QQ, Yang FT, Wang MH
PPARGC1A rs8192678 G>A polymorphism affects the severity of hepatic histological features and
nonalcoholic steatohepatitis in patients with nonalcoholic fatty liver disease
3863
Zhang RN, Shen F, Pan Q, Cao HX, Chen GY, Fan JG
Retrospective Cohort Study
Does endoscopic intervention prevent subsequent gastrointestinal bleeding in patients with left ventricular
assist devices? A retrospective study
3877
Palchaudhuri S, Dhawan I, Parsikia A, Birati EY, Wald J, Siddique SM, Fisher LR
Retrospective Study
Diverse expression patterns of mucin 2 in colorectal cancer indicates its mechanism related to the intestinal
mucosal barrier
3888
Gan GL, Wu HT, Chen WJ, Li CL, Ye QQ, Zheng YF, Liu J
Clinical characteristics of patients in their forties who underwent surgical resection for colorectal cancer in
Korea
3901
Lee CS, Baek SJ, Kwak JM, Kim J, Kim SH
Observational Study
Effect of gastric microbiota on quadruple Helicobacter pylori eradication therapy containing bismuth
3913
Niu ZY, Li SZ, Shi YY, Xue Y
META-ANALYSIS
Endoscopic submucosal dissection vs endoscopic mucosal resection for colorectal polyps: A meta-analysis
and meta-regression with single arm analysis
3925
Lim XC, Nistala KRY, Ng CH, Lin SY, Tan DJH, Ho KY, Chong CS, Muthiah M
CASE REPORT
Gastric schwannoma treated by endoscopic full-thickness resection and endoscopic purse-string suture: A
case report
3940
Lu ZY, Zhao DY
LETTER TO THE EDITOR
Gastrointestinal cytomegalovirus disease secondary to measles in an immunocompetent infant
3948
Hung CM, Lee PH, Lee HM, Chiu CC
WJG https://www.wjgnet.com III July 7, 2021 Volume 27 Issue 25
World Journal of Gastroenterology
Contents Weekly Volume 27 Number 25 July 7, 2021
ABOUT COVER
Editorial Board Member of World Journal of Gastroenterology, Paola De Nardi, MD, FASCRS, Doctor, Surgeon,
Surgical Oncologist, Division of Gastrointestinal Surgery, IRCCS San Raffaele Scientific Institute, via Olgettina 60,
Milan 20132, Italy. denardi.paola@hsr.it
AIMS AND SCOPE
The primary aim of World Journal of Gastroenterology (WJG, World J Gastroenterol) is to provide scholars and readers
from various fields of gastroenterology and hepatology with a platform to publish high-quality basic and clinical
research articles and communicate their research findings online. WJG mainly publishes articles reporting research
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including gastroenterology, hepatology, gastrointestinal endoscopy, gastrointestinal surgery, gastrointestinal
oncology, and pediatric gastroenterology.
INDEXING/ABSTRACTING
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IF without journal self cites: 5.590; 5-year IF: 5.044; Ranking: 28 among 92 journals in gastroenterology and
hepatology; and Quartile category: Q2. The WJG’s CiteScore for 2020 is 6.9 and Scopus CiteScore rank 2020:
Gastroenterology is 19/136.
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Submit a Manuscript: https://www.f6publishing.com World J Gastroenterol 2021 July 7; 27(25): 3734-3747
DOI: 10.3748/wjg.v27.i25.3734 ISSN 1007-9327 (print) ISSN 2219-2840 (online)
REVIEW
Artificial intelligence in small intestinal diseases: Application and
prospects
Yu Yang, Yu-Xuan Li, Ren-Qi Yao, Xiao-Hui Du, Chao Ren
ORCID number: Yu Yang 0000-
0002-0549-8755; Yu-Xuan Li 0000-
0003-2568-6347; Ren-Qi Yao 0000-
0002-3173-4301; Xiao-Hui Du 0000-
0002-8713-1358; Chao Ren 0000-
0003-0655-0209.
Author contributions: Yang Y
searched the literature for recent
advances in the field and wrote the
manuscript; Yang Y, Li YX, Yao RQ
and Du XH edited and revised the
manuscript; Ren C designed the
study; all authors approved the
final version to be published.
Supported by The National Natural
Science Foundation of China, No.
81871317.
Conflict-of-interest statement: The
authors declare that the research
was conducted in the absence of
any commercial or financial
relationships that could be
construed as a potential conflict of
interest.
Open-Access: This article is an
open-access article that was
selected by an in-house editor and
fully peer-reviewed by external
reviewers. It is distributed in
accordance with the Creative
Commons Attribution
NonCommercial (CC BY-NC 4.0)
license, which permits others to
distribute, remix, adapt, build
upon this work non-commercially,
and license their derivative works
Yu Yang, Yu-Xuan Li, Xiao-Hui Du, Department of General Surgery, Chinese People’s Liberation
Army General Hospital, Beijing 100853, China
Ren-Qi Yao, Chao Ren, Trauma Research Center, The Fourth Medical Center and Medical
Innovation Research Division of the Chinese People‘s Liberation Army General Hospital,
Beijing 100048, China
Ren-Qi Yao, Department of Burn Surgery, Changhai Hospital, Naval Medical University,
Shanghai 200433, China
Corresponding author: Chao Ren, MD, PhD, Trauma Research Center, Fourth Medical Center
and Medical Innovation Research Division of the Chinese People’s Liberation Army General
Hospital, No. 51 Fucheng Road, Beijing 100048, China. rc198@sina.com
Abstract
The small intestine is located in the middle of the gastrointestinal tract, so small
intestinal diseases are more difficult to diagnose than other gastrointestinal
diseases. However, with the extensive application of artificial intelligence in the
field of small intestinal diseases, with its efficient learning capacities and compu-
tational power, artificial intelligence plays an important role in the auxiliary
diagnosis and prognosis prediction based on the capsule endoscopy and other
examination methods, which improves the accuracy of diagnosis and prediction
and reduces the workload of doctors. In this review, a comprehensive retrieval
was performed on articles published up to October 2020 from PubMed and other
databases. Thereby the application status of artificial intelligence in small
intestinal diseases was systematically introduced, and the challenges and pro-
spects in this field were also analyzed.
Key Words: Artificial intelligence; Machine learning; Deep learning; Prognosis prediction;
Small intestinal diseases
©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
Core Tip: Artificial intelligence has been widely used in the management of small
intestinal diseases, which has greatly improved the diagnostic efficiency of capsule
Yang Y et al. Artificial intelligence in small intestinal diseases
WJG https://www.wjgnet.com 3735 July 7, 2021 Volume 27 Issue 25
on different terms, provided the
original work is properly cited and
the use is non-commercial. See: htt
p://creativecommons.org/License
s/by-nc/4.0/
Manuscript source: Invited
manuscript
Specialty type: Gastroenterology
and hepatology
Country/Territory of origin: China
Peer-review report’s scientific
quality classification
Grade A (Excellent): 0
Grade B (Very good): B, B
Grade C (Good): C, C
Grade D (Fair): 0
Grade E (Poor): 0
Received: January 25, 2021
Peer-review started: January 25,
2021
First decision: March 29, 2021
Revised: April 9, 2021
Accepted: May 8, 2021
Article in press: May 8, 2021
Published online: July 7, 2021
P-Reviewer: Chen CH, Jiang Y,
Orhan K, Pons-Beltrán V
S-Editor: Zhang H
L-Editor: Filipodia
P-Editor: Liu JH
endoscopy and other examination methods, and at the same time, beneficial
progression has also been obtained in the prognosis prediction of small intestinal
diseases. Although AI still faces risks such as overfitting and black box effects, its
stability and efficiency give it great potential in the management of small intestinal
diseases. This article reviews the current application status of AI in small intestinal
diseases. In addition, challenges and prospects in this field are discussed.
Citation: Yang Y, Li YX, Yao RQ, Du XH, Ren C. Artificial intelligence in small intestinal
diseases: Application and prospects. World J Gastroenterol 2021; 27(25): 3734-3747
URL: https://www.wjgnet.com/1007-9327/full/v27/i25/3734.htm
DOI: https://dx.doi.org/10.3748/wjg.v27.i25.3734
INTRODUCTION
The small intestine is located in the middle of the gastrointestinal digestive system,
with a total length of 5 m to 7 m, including the duodenum, jejunum and ileum, and is
the longest organ of the digestive system. Small intestinal diseases (SIDs) mainly
include celiac disease (CD), small intestinal Crohn’s disease (SICD), primary small
intestinal tumor (PSIT), obscure gastrointestinal bleeding and so on. The traditional
examination methods include X-ray barium enterography, computed tomography
(CT), magnetic resonance imaging (MRI), balloon-assisted enteroscopy, deep
enteroscopy and so on. In recent years, the emergence of capsule endoscopy (CE) has
brought a revolutionary breakthrough for the diagnosis of SIDs. However, because of
the special anatomical position of the small intestine (far away from the oral cavity and
anus, overlap and peristalsis), there are still many problems in the diagnosis of SIDs,
such as high technical requirements, low positive rate of diagnosis, inaccurate
qualitative location of the disease, patient intolerance and so on. In addition, the onset
of SIDs is insidious, the specificity of clinical symptoms is low, and the lesion site is
not easy to explore, so the clinical diagnosis of SIDs has always been a difficult
problem. With the emergence of artificial intelligence (AI) and its wide application in
the medical field, it also provides new methods for the whole management process of
SIDs and greatly improves the efficiency of SIDs management.
AI is a concept put forward in the 1950s. It is a frontier cross discipline developed
on the basis of computer science, neuropsychology, philosophy, linguistics, cyber-
netics, information theory and so on[1]. The research fields of AI include expert
system, machine learning (ML), fuzzy logic, natural language processing and so on.
Research methods are also developing continuously, from ML to deep learning and
then to convolutional neural network (CNN), promoting the rapid development of
research in various fields. The research of AI in the medical field is mainly focused on
auxiliary diagnosis. This series of methods of AI has become a hot implementation tool
in the field of medical imaging and digestive endoscope[2,3]. Taking the experiment of
the breast cancer AI detection system established by Google as an example, the
computer-aided diagnosis system based on AI can help doctors reduce the misdia-
gnosis rate of breast cancer by 5.7%[4]. Researchers at Houston Methodist Hospital
also said in their study that they have developed AI software that parses breast X-ray
images 30 times faster than ordinary doctors, with an accuracy of 99%[5]. AI is widely
used in the study of digestive fields such as gastric cancer[6], colorectal cancer[7],
esophageal cancer[8] and so on. AI has also been extensively researched in the field of
SIDs, which will be introduced in this paper.
This study used the keywords of “artificial intelligence” and “small intestine” to
search the relevant literature in the databases of PubMed, Embase, Web of Science and
Cochrane Library up to October 2020. Studies included in our review were required to
meet the following inclusion criteria: (1) full-text paper available in English; and (2)
studies that associated AI with the small intestinal diseases. We excluded descriptive
papers without validation of methods. The application status of AI in SIDs was
summarized, and the challenges and prospects in this field were discussed.
Yang Y et al. Artificial intelligence in small intestinal diseases
WJG https://www.wjgnet.com 3736 July 7, 2021 Volume 27 Issue 25
AI IN SMALL INTESTINE ANATOMY
Organ segmentation of the small intestine
With the advent of AI, it is possible to perform computer-assisted organ segmentation
in CT, MRI, endoscopy and other examination methods and has shown good
application potential in the fields like assisted localization of radiotherapy. The
following will introduce the research progress of this aspect in the field of the small
intestine especially the duodenum (Table 1).
CT: Some studies had used the CNN method to automatically segment duodenum
and other abdominal organs from CT images, with clinically acceptable accuracy and
efficiency[9,10]. Tong et al[11] proposed an end-to-end segmentation network for
improving multiorgan segmentation performance using the ML method. The dice
similarity coefficient and average surface distance were quantitatively evaluated, and
the results confirmed this network had good accuracy and timeliness in the anatomical
segmentation of abdominal organs including the duodenum.
MRI: Fu et al[12] conducted a retrospective analysis on 3D MR images of 120 patients
and proposed a CNN model, which has been verified to accurately segment the
abdominal organs including the duodenum and expedite the contouring process for
MRI-guided adaptive radiotherapy. Chen et al[13] also conducted a similar study, and
in their study, the inference process was completed within 1 min, indicating an
obvious advantage of timeliness. The length of the small intestine is an important
factor in the management of patients with short bowel syndrome. Some scholars
designed a special software algorithm to calculate the length of small intestine based
on magnetic resonance enterography images in mice. Compared with the measured
results of anatomical specimens, the mean absolute difference between the two
methods was 1.8 ± 3.8 cm (P = 0.24), and the mean percentage difference was 9.4% ±
6.0%[14].
Endoscopy: In a Japanese study based on GoogLeNet architecture, a CNN diagnostic
program was constructed, using 27335 esophagogastroduodenoscopy (EGD) images
for the training set and using 17081 EGD images for the independent validation set.
The results showed that the CNN has a good effect to classify the anatomical location
of EGD images for stomach and duodenum images, with an area under the curve of
0.99[15]. Igarashi et al[16] used AlexNet (a deep learning framework) to retrospectively
analyze 85246 original images of EGD images in 441 patients with gastric cancer and
developed an anatomical organ classifier. The accuracy rates of the training and
validation sets were 0.993 and 0.965, respectively.
Diagnosis of small intestinal mucosal lesions
With the emergence of CE in 2000[17], it has revolutionized our understanding of
small intestinal mucosa[18-20], enabling doctors to detect small intestinal mucosal
erosion, ulcers, vascular disease, bleeding, polyps, parasite and other lesions more
efficiently. However, reliable and rapid reading of video is still a challenge, but more
and more studies have shown that the combination of AI and CE can greatly improve
the efficiency of our evaluation of small intestinal mucosal lesions; the detection
accuracy was above 90% in most studies[21-27].
Ulcer: Previous studies have confirmed that applying a CNN system of deep learning
to the reading process of CE can reduce the reading time without decreasing the
detection rate of erosion and ulcer lesions[28-32].
Angioectasias and bleeding: Intestinal angioectasias cause more than 8% of all
gastrointestinal bleeding episodes[33]. Different studies have applied ML, CNN and
computer algorithms to the differential diagnosis of intestinal angioectasias and have
achieved high sensitivity and specificity[34-39]. AI is also applied to the direct
examination of intestinal mucosal bleeding by CE, which can directly calculate the
blood content in the digestive tract and infer whether there is active bleeding in the
small intestinal mucosa[40-44].
Protruding lesions: There are a variety of small intestinal mucosal protruding lesions.
CNN can help doctors describe their shape features, help analyze their nature and
distinguish polyps, epithelial tumors, submucosal tumors, etc.[34,45,46].
Villous atrophy: Villous atrophy is a defining symptom of some digestive tract
diseases such as CD. Some scholars combined AI methods with CE for the detection
Yang Y et al. Artificial intelligence in small intestinal diseases
WJG https://www.wjgnet.com 3737 July 7, 2021 Volume 27 Issue 25
Table 1 Applications of artificial intelligence in organ segmentation of the small intestine
Ref. Diagnostic method AI technology Training set Validating set Outcomes
Tong et al[11] CT ML 90 images - DSC of duodenum: 69.26%
Kim et al[9] CT CNN 80 images 40 images DSC of duodenum: 0.595
Peng et al[10] CT CNN 43 images - DSC of duodenum: 0.61
Dice coefficient of duodenum:
65.50% ± 8.90%
Fu et al[12] MRI CNN 100 images 20 images
Dice coefficient of bowel: 86.60%
± 2.69%
Chen et al[13] MRI DL 66 images 36 images DSC of duodenum: 0.80
Takiyama et al[15] EGD CNN 27335 images 17081 images AUCs: 0.99
Igarashi et al[16] EGD ML 49174 images 36072 images Accuracy (Ts: 0.993, Vs: 0.965)
AI: Artificial intelligence; AUCs: Area under the curves; CNN: Convolutional neural network; CT: Computed Tomography; DL: Deep learning; DSC: Dice
similarity coefficient; EGD: Esophagogastroduodenoscopy; ML: Machine learning; MRI: Magnetic resonance imaging; Ts: Training set; Vs: Validating set.
and measurement of villous atrophy and successfully mapped the extent of the
diseased small intestine[47].
AI is also used in risk prediction and clinical treatment decisions of small intestinal
mucosal lesions. For example, one study used CNN for the risk prediction of acute
intestinal bleeding[48], and another study applied CNN to risk prediction and
therapeutic tactics selection for duodenal ulcers[49]. Wong et al[50] built a ML model,
based on data from 22854 patients with gastroduodenal ulcer including six clinical
parameters to identify patients at high risk for recurrent ulcer bleeding within 1 year.
Gastrointestinal bleeding is a common complication of left ventricular assist device
treatment. Axelrad et al[51] developed an endoscopic algorithm. Compared with
conventional cohorts, the implementation of the algorithm increased endoscopic
diagnostic efficiency by 68%, treatment efficiency by 113%, the number of procedures
per patient decreased by 27%, the length of hospital stay decreased by 33%, and the
estimated cost decreased by 18%.
In addition, the interference of intestinal contents to CE can also be reduced by AI.
Combined with support vector machine, Bashar et al[52] designed a classifier for
separating useless frames that are highly contaminated by turbid fluids, fecal materials
and/or residual foods. The accuracy of this classifier was more than 80%. Pietri et al
[53] developed a computer algorithm to automatically evaluate the demeanor of small
intestinal bubbles in CE images. The specificity of this algorithm was 95.79%, the
sensitivity was 95.19%, and the calculation time was 0.037 s per frame. It can be used
to reduce the interference of bubbles in CE images. Klein et al[54] created a computed
algorithm based on the pixels in the color bar to score and classify the preparation of
the small intestine for CE, and this automatic scoring method has a concordance rate of
more than 90% with the assessment of clinicians.
AI IN COMMON SMALL INTESTINAL DISEASES
AI in celiac disease
CD is a complex autoimmune disease. Patients who ingest foods containing gluten will
develop an autoimmune response that causes damage to the small intestine. CD is one
of the most common chronic digestive diseases, with a prevalence rate of 1%
worldwide[55]. Duodenal biopsy is the gold standard for diagnosis[56]. Noninvasive
methods such as endoscopy and clinical features analysis are also widely used in
diagnosis, but the diagnostic rate of CD is only 15%–20% through current strategies
[57]. However, with the increasing application of AI in the diagnosis of CD, the
accuracy and efficiency of diagnosis are greatly improved[58] (Table 2).
Previous studies have confirmed that AI-assisted duodenoscope images analysis can
greatly improve the diagnostic efficacy of CD, with the accuracy between 80% and
100% and specificity and sensitivity over 80%[59-61]. In the diagnosis of CD, the
combination of AI and CE is closer, which can improve the accuracy of diagnosis and
Yang Y et al. Artificial intelligence in small intestinal diseases
WJG https://www.wjgnet.com 3738 July 7, 2021 Volume 27 Issue 25
Table 2 Applications of artificial intelligence in celiac disease
Ref. Diagnostic
method AI technology Training set Testing set Outcomes
Chetcuti et al[62] CE ML 81 patients - Accuracy: 75.3%
Li et al[63] CE Computer-assisted recognition Ep: 240, Cp: 220 - Accuracy: 93.9%
Vicnesh et al[64] CE Computerized algorithm 21 patients - Accuracy: 89.82%
Zhou et al[65] CE CNN Ep: 6, Cp: 5 Ep: 5, Cp: 5 Accuracy: 100%
Gadermayr et al
[59]
EGD Computer-assisted 290 patients (2835
images)
- Accuracy: 94%-100%
Das et al[67] Mucosal biopsies Computer-assisted Ep: 124, Cp: 137 Ep: 120, Cp:
105
Sen: 90.3%, Spe: 93.5%,
AUCs: 96.2%
Wei et al[66] Mucosal biopsies DL 212 images - Accuracy: 95.3%, AUCs >
0.95
Pastore et al[70] Clinical data Computer-assisted 100 patients - Reliability: 0.813
Tenório et al[60] Clinical data Decision trees, Bayesian inference, k-nearest
neighbor algorithm, support vector machines,
artificial neural networks
178 patients 38 patients Accuracy: 80.0%, Sen:
0.78, Spe: 0.80, AUCs: 0.84
Virta et al[68] Micro-CT Computer-assisted point cloud analysis 81 patients - Accuracy: 100%
Sangineto et al[69] Gene expression in
PBMCs
ML, random forest algorithm Ep: 17, Cp: 20 - Accuracy: 100%
AI: Artificial intelligence; AUCs: Area under the curves; CE: Capsule endoscopy; CNN: Convolutional neural network; Cp: Control group; DL: Deep
learning; EGD: Esophagogastroduodenoscopy; Ep: Experimental group; ML: Machine learning; micro-CT: X-ray microtomography; PBMCs: Peripheral
blood mononuclear cells; Sen: Sensitivity; Spe: Specificity.
significantly save the diagnosis time[62-65]. AI was also used in the analysis of
duodenal mucosa biopsy, which can help with qualitative analysis and play an
important role in quantitative analysis[66,67]. At the same time, the application of AI
with X-ray images[68], peripheral blood mononuclear cells[69] and clinical features[60,
70] in the diagnosis and classification of CD have also achieved progress.
AI in small intestinal Crohn’s disease
Crohn’s disease is a chronic nonspecific inflammatory bowel disease that affects the
entire gastrointestinal tract, in which 30% of patients are confined to the small
intestine, commonly known as small intestinal Crohn‘s disease[71]. SICD most often
involves the distal ileum as well as the jejunum and the digestive tract above and has a
higher incidence of intestinal strictures than colonic Crohn‘s disease[72,73]. The
application of AI in the management of SICD is comprehensive, including diagnosis,
risk prediction, extra-intestinal manifestation (EIM) prediction and so on (Table 3).
Diagnosis: Lamash et al[74,75] used CNN to analyze MRI images and construct an
assessment model for SICD. Their model could effectively distinguish active and
inactive inflammatory segments, distinguish segments with strictures and segments
without strictures and could be used to measure the length of intestinal strictures.
Parfеnov et al[76] used a software diagnostic algorithm to analyze the CE images of 25
SICD patients, preliminarily confirming that CE could be used to diagnose early SICD
with intestinal mucosal inflammation. Klang et al[77] performed automatic analysis of
CE images of 49 SICD patients using a CNN method, achieving diagnostic accuracy of
more than 95% and significantly reducing reading time. Yang et al[78] also attempted
to combine CNN with a microultrasound system for early diagnosis of SICD in mice
and achieved good effectiveness in the identification of early inflammation.
Risk prediction of SICD: Taylor et al[79] used ML classifiers (elastic network and
random forest) to classify small intestine inflammation in asymptomatic first-degree
relatives of patients with SICD. They found that genetic variants associated with SICD,
family history and fecal calprotectin together identified individuals with presymp-
tomatic intestinal inflammation who are therefore at risk for SICD. Shen et al[80]
developed a web-based SICD hazard stratification tool. Predicting high-risk
populations for SICD based on altered bowel habit, abdominal pain, white blood cell
Yang Y et al. Artificial intelligence in small intestinal diseases
WJG https://www.wjgnet.com 3739 July 7, 2021 Volume 27 Issue 25
Table 3 Applications of artificial intelligence in small intestinal Crohn’s disease
Ref. Diagnostic method AI technology Training set Testing set Outcomes
Yang et al[78] Microultrasound CNN 43 mice - AUCs: 0.8831
Shen et al[80] Clinical data Computerized
algorithm
Ep1: 61, Cp1: 78 Ep2:42, Cp2: 57;
Ep3:84, Cp3: 495
AUCs: 0.92
Bottigliengo et al
[81]
Clinical data BMLTs (NB, BN,
BART)
152 patients - AUCs without genetic variables (NB: 0.71,
BN: 0.50, BART: 0.76), AUCs with genetic
variables (NB: 0.75, BN: 0.67, BART: 0.78)
Taylor et al[79] Clinical data ML (elastic net and
random forest)
480 first-degree
relatives
- AUCs (elastic net): 0.80, AUCs (random
forest): 0.87
Menti et al[82] Clinical data BMLTs 152 patients - Accuracy without genetic variables: 82%,
accuracy with genetic variables: 89%
Klang et al[77] CE DL 49 patients
(17640 images)
- AUCs: 0.94-0.99, accuracy: 95.4%-96.7%
Parfеnov et al[76] CE Computerized
algorithm
25 patients - 44% patients confirmed only with the help
of AI
Lamash et al[74,
75]
MRI CNN 15 patients 8 patients Dice coefficients: 75%-97%
AI: Artificial intelligence; AUCs: Area under the curves; BART: Bayes additive return trees; BMLTs: Bayesian machine learning techniques; BN: Bayesian
network; CE: Capsule endoscopy; DL: Deep learning; CNN: Convolutional neural network; Cp: Control group; Ep: Experimental group; ML: Machine
learning; MRI: Magnetic resonance imaging; NB: Naive Bayes.
count, albumin and platelet count abnormalities allowed clinicians to identify
potential SICD earlier.
Risk prediction of EIMs: AI is controversial in the evaluation of the EIMs of SICD. In
the study of Bottigliengo et al[81], based on Bayesian machine learning technology
evaluation combined with genetic factors to predict the occurrence of EIMs in Crohn’s
disease, it has no advantage over traditional statistical tools. Whereas Menti et al[82]
used Bayesian machine learning technology to predict the risk of occurrence of EIMs
in Crohn’s disease, and the prediction accuracy was 82% when considering only
clinical factor and 89% combined with genetic factors, which was outperforming other
prediction techniques.
AI in primary small intestinal tumor
The incidence of PSIT is about 5% of gastrointestinal tumors and 0.2% of all kinds of
tumors[83,84]. The main site of PSIT is the duodenum, followed by the jejunum and
ileum[85]. There are a variety of pathological types of malignant PSIT. Adenocar-
cinoma is the most common pathological type, up to 40%, followed by neuroendocrine
tumors (25%), malignant lymphomas (10%-15%) and malignant stromal tumors (9%)
[86]. PSIT lacks specific manifestations in the early stage, and they are faced with many
problems in the clinic, such as difficult diagnosis, high misdiagnosis rate, nonstandard
treatment and so on[87]. AI has been applied in the field of auxiliary diagnosis and
prognostic analysis of PSIT, and has an important impact on the management
(Table 4).
Diagnosis: Inoue et al[88] used CNN to analyze EGD images for the diagnosis of
superficial nonampullary duodenal epithelial tumors. The overall diagnosis accuracy
of CNN was 94.7%, including 94% for adenomas and 100% for high-grade dysplasias,
and it only took 12-31 s for analysis. The method of support vector machine was
applied to the automatic analysis of CE images, which greatly improved the accuracy
and efficiency of diagnosis[89-92]. In addition, Barbosa et al[93] used the CNN to
automatically analyze CE images for the diagnosis of PSIT, which also had high
sensitivity and specificity, reaching 98.7% and 96.6%, respectively.
Risk stratification and prognosis prediction: In different studies, ML was used to
analyze the pathological tissue samples, plasma protein multibiomarker and miRNA
markers of patients with small intestinal neuroendocrine tumors[94-96]. Their studies
provided some new and effective methods for early diagnosis, treatment strategy
selection, prognosis prediction and recurrence risk prediction of small intestinal
Yang Y et al. Artificial intelligence in small intestinal diseases
WJG https://www.wjgnet.com 3740 July 7, 2021 Volume 27 Issue 25
Table 4 Applications of artificial intelligence in primary small intestinal tumor
Ref. Diagnostic method AI technology Training set Testing set Outcomes
Inoue et al[88] EGD CNN 531 images 1080 images Accuracy: 94.7%-100%
Liu et al[90] CE SVM 89 patients - Sen: 97.8%, Spe: 96.7%
Vieira et al[89,91] CE SVM 29 patients (936
images)
- This SVM outperforms others by more than
5%
Barbosa et al[93] CE CNN Ep: 104, Cp: 100 Ep: 92, Cp: 100 Sen: 98.7%, Spe: 96.6%
Panarelli et al[94] MicroRNA sequencing ML 84 samples - Accuracy (Ts: 98.5%, Vs: 94.4%)
Drozdov et al[95] Gene expression profiling ML 73 samples - Differentiated from normal cells (Sen: 100%,
Spe: 92%), metastases prediction (Sen:
100%, Spe: 100%)
Kjellman et al[96] Plasma protein
multibiomarker
Random forestmodel Ep:135, Cp: 143 - AUCs: 0.97
Yan et al[97] CT Random forestmodel 213 patients - AUCs: 0.943
AI: Artificial intelligence; AUCs: Area under the curves; CE: Capsule endoscopy; CNN: Convolutional neural network; CT: Computed tomography; Cp:
Control group; EGD: esophagogastroduodenoscopy; Ep: Experimental group; ML: Machine learning; SVM: Support vector machine; Sen: Sensitivity; Spe:
Specificity; Ts: Training set; Vs: Validating set.
neuroendocrine tumors. In the study of Yan et al[97], random forest models were
performed to evaluate the correlation of risk stratification for small intestinal stromal
tumors. Their study suggested multidetector CT texture analysis may become an
important comprehensive tool for preoperative risk stratification of small intestinal
stromal tumors.
AI in other small intestinal diseases
Small intestinal obstruction: Cheng et al[98,99] used CNN to analyze abdominal
radiographs to assist in the diagnosis of small intestinal obstruction (SIO). The
sensitivity and specificity of the CNN diagnostic system were 83.8% and 68.1%,
respectively, based on the training set of 2210 abdominal radiographs. When the
training set was expanded to 7768 abdominal radiographs, the diagnostic sensitivity
and specificity were increased to 91.4% and 91.9%, respectively. Their study suggests
that the accuracy of detection of SIO by CNN improves significantly with the
increasing number of training radiographs. Lucas et al[100] explored the development
of an ML tool for SIO detection based on CT images. They evaluated the accuracy of
eye tracking in image centerline annotation of the small intestine as the first step in the
development of an ML tool for SIO. Their results showed that the eye tracking-based
annotation was accurate and precise enough for application in ML-based small
intestinal centerline annotation.
Small intestinal motor dysfunction: Small bowel intestinal dysfunction (SIMD) can
occur during the development of many diseases, so the evaluation of small intestinal
motor function is an important means for the auxiliary diagnosis and severity
evaluation of these diseases. AI is widely used to evaluate small intestinal motor
function through CE images. Using intraintestinal manometry as the gold standard for
the diagnosis of SIMD, Malagelada et al[101] proved that an ML model was reliable to
evaluate CE images for the diagnosis of SIMD. Applying this model, they found that
29% of patients with functional intestinal disorders had SIMD, significantly higher
than that of the healthy population (3%), confirming the pathophysiological changes in
the intestine of functional intestinal disorders[102]. Furthermore, a classification
method for classifying functional intestinal disorders according to small intestinal
motor function was also proposed[103].
De Iorio et al[104] also demonstrated that ML can reliably detect reduced intestinal
muscle activity and motion by CE images through the method of injecting intestinal
muscle inhibition (glucagon) into healthy subjects. Moreover, the study of Seguí et al
[105] suggested that CNN was also reliable in the description and classification of
small intestine motor characteristics with the classification accuracy reaching 96%.
Malagelada et al[106] also used ML to analyze CE and abdominal MRI images of
patients with cystic fibrosis and confirmed that the delay in small bowel and colonic
transit times in patients with cystic fibrosis is associated with known endocrine
Yang Y et al. Artificial intelligence in small intestinal diseases
WJG https://www.wjgnet.com 3741 July 7, 2021 Volume 27 Issue 25
dysfunction and with SIMD.
Small intestinal ischemia-reperfusion injury: Intraoperative evaluation of intestinal
viability in patients with acute intestinal ischemia is a critical factor for surgical
decision making. In the pig jejunum experiment, Strand-Amundsen et al[107]
attempted to apply ML to the analysis of multivariate time-series of bioimpedance
sensor data to analyze intestinal viability after intestinal ischemia-reperfusion. The
results suggested that the measurement should be made before the onset of
reperfusion, and the prediction effect was better when the measurement was repeated
continuously during ischemia and reperfusion. The detection accuracy of irreversible
damage may be close to 100%.
Enteropathies associated with undernutrition: There is a significant histopathological
overlap in duodenal biopsies of enteropathies associated with undernutrition such as
environmental enteropathy and CD. Syed et al[108] used CNN to establish a histopath-
ological analysis model, which can effectively distinguish environmental enteropathy,
CD and normal intestinal mucosa. The detection accuracy was 93.4%, and the false-
negative rate was 2.4%.
CHALLANGES AND PROSPECTS
At present, in order to promote the application of AI in the field of SIDs, we still need
to solve some problems and challenges: (1) Insufficiency of training sample size. The
incidence of most SIDs is not high. For example, the incidence of PSIT is much lower
than that of gastric tumors and colorectal tumors, which affects the amount of data in
the training set. Measurement errors are easy to occur when the sample size is small
[109], and as suggested by the continuity study of Cheng et al[98,99], expanding the
sample size of the training set can significantly improve the inspection accuracy of AI
model; (2) Lack of prospective data. Most of the studies used retrospective data, which
have been artificially screened before AI model training and lack prospective studies;
(3) Single source of data. Most of the training sets and verification sets used in the
study come from single-center data, so it is still necessary to further improve the
repeatability and stability of the model through multicenter data. As in Alzheimer‘s
disease research, each single center should be encouraged to share data in anticipation
of establishing large-scale and open databases[110]; (4) The interpretation of the
results. Due to the inevitable problems of AI, like overfitting of training set data[111]
and the “black box” characteristic of the algorithm[112], the accuracy and
interpretation of the AI model are inconsistent, which may have a negative impact on
clinical application[113]. More intensive basic research and extensive verification are
needed to improve this deficiency; (5) Ethical and legal issues. Can we trust the results
of AI? Once the AI diagnosis and treatment prediction fails, it will give rise to a series
of social, ethical and legal problems[114,115]. It is necessary to combine human
supervision with AI tools more reliably; and (6) AI has been widely researched in
various fields. We should attach importance to learning experience from different
research fields and try to carry out related research in the field of SIDs, so as to
promote the continuous progress of AI research in the field of SIDs.
CONCLUSION
The advantages of AI in the diagnosis and prognosis analysis of SIDs have been
increasingly recognized, and the high accuracy and efficiency of AI detection greatly
reduce the workload of doctors. Although there are still various challenges in the
application of AI, the potential of AI in improving the management efficiency of
diseases cannot be ignored. Clinicians should work together with experts in various
fields to promote the development of AI in SIDs.
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