• adverse event
• colon capsule endoscopy
• compliance
• out‐of‐clinic setting
• small bowel capsule endoscopy

• artificial intelligence
• colon capsule endoscopy
• convolutional neural network

• advanced colorectal neoplasia
• polypectomy
• screening

• Humans
• Male
• Middle Aged
• Female
• Colorectal Neoplasms/epidemiology
• Colorectal Neoplasms/diagnosis
• Colorectal Neoplasms/pathology
• Retrospective Studies
• Risk Assessment/methods
• Colonoscopy/statistics & numerical data
• Aged
• Colonic Polyps/pathology
• Colonic Polyps/diagnosis
• Risk Factors
• ROC Curve
• Hong Kong/epidemiology
• Adult
• Logistic Models

• 14116820 University Grants Committee-General Research Fund
• PDFS2223-4S03 Research Grant Council (RGC) Postdoctoral Fellowship Scheme

• Colonoscopy
• Colorectal cancer screening
• Screening modalities
• Screening risk stratification
• Screening schedule

• Humans
• Colonoscopy/methods
• Colorectal Neoplasms/diagnosis
• Colorectal Neoplasms/prevention & control
• Early Detection of Cancer/methods
• Mass Screening/methods
• Occult Blood
• United States/epidemiology
• Practice Guidelines as Topic

• colon capsule endoscopy
• endoscopy
• pediatric IBD
• ulcerative colitis
• ultrasonography

• Male
• Child
• Humans
• Adolescent
• Colitis, Ulcerative/diagnostic imaging
• Prospective Studies
• Follow-Up Studies
• Reproducibility of Results
• Severity of Illness Index
• Colonoscopy/methods
• Feces
• Leukocyte L1 Antigen Complex
• Biomarkers

• None

• Bowel preparation
• Capsule colonoscopy
• Pan-intestinal capsule endoscopy

• Humans
• Cathartics
• Laxatives
• Capsule Endoscopy/methods
• Colonoscopy/methods
• Polyethylene Glycols

• Universidade do Minho

• control strategies
• endoscopic robot
• model-based control
• model-free control
• soft medical robots
• soft pneumatic actuator

• gastrointestinal diseases
• computer science

• Female
• Humans
• Capsule Endoscopy/methods
• Capsule Endoscopes
• Gastrointestinal Tract
• Magnetic Resonance Imaging
• Uterus

• Norges Forskningsråd
• NTNU Norwegian University of Science and Technology (incl St. Olavs Hospital - Trondheim University Hospital)

• cancer
• colorectal
• endoscopic capsule
• endoscopy
• virtual colonoscopy screening

• cancer
• colon
• colon cancer
• colon cancer prevention
• colon cancer surveillance
• colon polyps
• colonoscopy
• colorectal cancer
• fit test
• fobt

Endoscopy artifacts are widespread in real capsule endoscopy (CE) images but not in high-quality standard datasets.

To improve the segmentation performance of polyps from CE images with artifacts based on ensemble learning.

We collected 277 polyp images with CE artifacts from 5760 h of videos from 480 patients at Guangzhou First People’s Hospital from January 2016 to December 2019. Two public high-quality standard external datasets were retrieved and used for the comparison experiments. For each dataset, we randomly segmented the data into training, validation, and testing sets for model training, selection, and testing. We compared the performance of the base models and the ensemble model in segmenting polyps from images with artifacts.

The performance of the semantic segmentation model was affected by artifacts in the sample images, which also affected the results of polyp detection by CE using a single model. The evaluation based on real datasets with artifacts and standard datasets showed that the ensemble model of all state-of-the-art models performed better than the best corresponding base learner on the real dataset with artifacts. Compared with the corresponding optimal base learners, the intersection over union (IoU) and dice of the ensemble learning model increased to different degrees, ranging from 0.08% to 7.01% and 0.61% to 4.93%, respectively. Moreover, in the standard datasets without artifacts, most of the ensemble models were slightly better than the base learner, as demonstrated by the IoU and dice increases ranging from -0.28% to 1.20% and -0.61% to 0.76%, respectively.

Ensemble learning can improve the segmentation accuracy of polyps from CE images with artifacts. Our results demonstrated an improvement in the detection rate of polyps with interference from artifacts.

• Artifacts
• Capsule endoscopy
• Polyps
• Ensemble learning
• Segmentation
• Robustness

• Humans
• Capsule Endoscopy/methods
• Artifacts
• Deep Learning
• Polyps/diagnostic imaging
• Gastrointestinal Neoplasms

• endoscopy
• future endoscope
• hyperspectral
• model-based systems engineering (MBSE)
• subsystem trends
• system architecture
• system lifecycle

• P01 HL066299 NHLBI NIH HHS
• UL1 TR001417 NCATS NIH HHS
• UL1 TR003096 NCATS NIH HHS

Colon screening programs have reduced colon cancer mortality. Population screening should be minimally invasive, safe, acceptably sensitive, cost-effective, and scalable. The range of screening modalities include guaiac or immunochemical fecal occult blood testing and CT colonography and colonoscopy. A number of carefully controlled studies concur that second-generation capsule endoscopy has excellent sensitivity for polyp detection and a high negative predictive value. Colon capsules fulfill the screening expectation of safety, high sensitivity for polyp detection, and patient acceptance, and appear to straddle the divide between occult blood testing and colonoscopy. While meeting these criteria, there remains the challenges of scaling, capsule practitioner training, resource allocation, and implementing change of practice. Like CT colonography, capsule screening presents the clinician with a decision on the threshold for colonoscopy referral. Overall, colon capsules are an invaluable tool in polyp detection and colon screening and offer a filter that determines “who needs a colonoscopy?”.

• Bowel preparation
• Capsule
• Cathartics
• Colorectal cancer
• Polyps
• Screening

Colorectal cancer (CRC) is a heterogeneous illness characterized by various epigenetic and microenvironmental changes and is the third-highest cause of cancer-related death in the US. Artificial intelligence (AI) with its ability to allow automatic learning and improvement from experiences using statistical methods and Deep learning has made a distinctive contribution to the diagnosis and treatment of several cancer types. This review discusses the uses and application of AI in CRC screening using automated polyp detection assistance technologies to the development of computer-assisted diagnostic algorithms capable of accurately detecting polyps during colonoscopy and classifying them. Furthermore, we summarize the current research initiatives geared towards building computer-assisted diagnostic algorithms that aim at improving the diagnostic accuracy of benign from premalignant lesions. Considering the evolving transition to more personalized and tailored treatment strategies for CRC, the review also discusses the development of machine learning algorithms to understand responses to therapies and mechanisms of resistance as well as the future roles that AI applications may play in assisting in the treatment of CRC with the aim to improve disease outcomes. We also discuss the constraints and limitations of the use of AI systems. While the medical profession remains enthusiastic about the future of AI and machine learning, large-scale randomized clinical trials are needed to analyze AI algorithms before they can be used.

• Artificial intelligence
• Machine learning
• Colonic polyps
• Colorectal neoplasms
• Computer-aided diagnosis
• Precision oncology

• CC-CLEAR
• bowel preparation
• colon capsule
• multicentric validation
• quality criteria

• Aged
• Aged, 80 and over
• Colonoscopy/adverse effects
• Colorectal Neoplasms/diagnosis
• Colorectal Neoplasms/surgery
• Humans
• Nonagenarians
• Proportional Hazards Models
• Retrospective Studies
• Risk Factors

The COVID-19 pandemic has caused considerable disruption in healthcare services and has had a substantial impact on the care of patients with chronic diseases, such as inflammatory bowel disease. Endoscopy services were significantly restricted, resulting in long waiting lists. There has been a growing interest in the use of capsule endoscopy in the diagnostic pathway and management of these patients. This review explores the published literature on the role of colon capsule endoscopy in ulcerative colitis and Crohn’s disease as a method for mucosal assessment of extent, severity, and response to treatment. Colon capsule preparation regimens and scoring systems are reported. The studies indicate that, despite inherent limitations of minimally invasive capsule endoscopy, there is increasing evidence to support the use of the second-generation colon capsule in inflammatory bowel disease evaluation, providing an additional pathway to expedite investigation of appropriate patients especially during and after the pandemic.

• Algorithms
• Capsule Endoscopy/methods
• Deep Learning
• Humans
• Prospective Studies
• Sensitivity and Specificity

• CRC biomarkers
• colorectal cancer
• diagnosis
• epigenetic testing
• screening

• Biomarkers
• Colorectal Neoplasms/diagnosis
• Colorectal Neoplasms/genetics
• Early Detection of Cancer/methods
• Epigenesis, Genetic
• Humans
• Occult Blood
• United States

• capsule
• endoscopy
• small bowel

Image-enhanced endoscopy is useful for diagnosing and identifying lesions in the gastrointestinal tract. Recently, image-enhanced endoscopy has become a breakthrough technology that has attracted significant attention. This image enhancing technology is available for capsule endoscopy, which is an effective tool for small intestinal lesions and has been applied in flexible spectral color enhancement technology and in contrast capsule like narrow-band imaging. In this field, most researchers focus on improving the visibility and detection of small intestinal lesions. This review summarizes previous studies on image-enhanced capsule endoscopy and aims to evaluate the efficacy of this technology.

• capsule endoscopy
• contrast capsule
• flexible spectral color enhancement
• image-enhanced endoscopy
• small intestinal lesions

• capsule endoscopy
• clinical impact
• diagnostic yields

• Colonic diseases
• Colonic neoplasms
• Tomography X-ray computed

• NaP
• PEG
• capsule endoscopy
• cleansing
• colon
• diagnostic yield
• lavage
• oesophagus
• pan-intestinal
• prokinetics
• simethicone
• small bowel
• stomach
• visualization of mucosa

Preparation for colon capsule endoscopy (CCE) requires a large liquid laxative volume for capsule excretion, which compromises the procedure's tolerability.

To assess the safety and utility of castor oil-boosted bowel preparation.

This prospective cohort study including 20 patients (age range, 16-80 years; six men and 14 women) suspected of having colorectal disease was conducted at Kindai University Hospital from September 2017 to August 2019. All patients underwent CCE because of the following inclusion criteria: previous incomplete colonoscopy in other facility (n = 20), history of abdominal surgery (n = 7), or organ abnormalities such as multiple diverticulum (n = 4) and adhesion after surgery (n = 6). The exclusion criteria were as follows: Dysphagia, history of allergic reactions to the drugs used in this study (magnesium citrate, polyethylene glycol, metoclopramide, and castor oil), possibility of pregnancy, possibility of bowel obstruction or stenosis based on symptoms, or scheduled magnetic resonance imaging within 2 wk after CCE. The primary outcome was the capsule excretion rate within the battery life, as evaluated by the total large bowel observation rate, large bowel transit time, and bowel creasing level using a five-grade scale in different colorectal segments. The secondary outcomes were complications, colorectal lesion detection rates, and patients’ tolerability.

The castor oil-based regimen was implemented in 17 patients. Three patients cancelled CCE because they could tolerate castor oil, but not liquid laxatives. The capsule excretion rate within the battery life was 88% (15/17). The mean large bowel transit time was 236 min. Approximately 70% of patients had satisfactory colon cleansing levels. CCE detected colon polyps (14/17, 82%) and colonic diverticulum (4/12, 33%). The sensitivity, specificity, and diagnostic accuracy rates for detecting colorectal polyps (size ≥ 6 mm) were 76.9%, 75.0%, and 76.4%, respectively. The sensitivity, specificity, and diagnostic accuracy rates for detection of diverticulum were 100% each. Twelve patients (71%) rated CCE as more than “good”, confirming the new regimen’s tolerability. No serious adverse events occurred during this study.

The castor oil-based regimen could reduce bowel preparation dose and improve CCE tolerability.

• Bowel preparation regimen
• Castor oil
• Colon capsule endoscopy
• Colonoscopy
• Colorectal diseases
• Prospective study

• adenomatous polyp
• colon capsule endoscopy
• colorectal polyp
• flexible spectral imaging color enhancement
• hyperplastic polyp

• Capsule Endoscopy
• Colonic Polyps/diagnostic imaging
• Colonoscopy
• Colorectal Neoplasms/diagnosis
• Diagnosis, Differential
• Humans
• Retrospective Studies

In the constantly developing era of minimal diagnostic invasiveness, the role of colon capsule endoscopy in colonic examination is being increasingly recognised, especially in the context of curtailed endoscopy services due to the COVID-19 pandemic. It is a safe diagnostic tool with low adverse event rates. As with other endoscopic modalities, various colon capsule endoscopy scores allow the standardisation of reporting and reproducibility. As bowel cleanliness affects CCE’s diagnostic yield, a few operator-dependent scores (Leighton–Rex and CC-CLEAR scores) and a computer-dependent score (CAC score) have been developed to grade bowel cleanliness objectively. CCE can be used to monitor IBD mucosal disease activity through the UCEIS and the panenteric CECDAIic score for UC and CD, respectively. CCE may also have a role in CRC screening, given similar sensitivity and specificity rates to conventional colonoscopy to detect colonic polyps ≥ 10 mm and CRC. Given CCE’s diagnostic yield and reproducible clinical scores with high inter-observer agreements, CCE is fast becoming a suitable alternative to conventional colonoscopy in specific patient populations.

• bowel preparation
• colon capsule endoscopy
• colonoscopy
• endoscopy score
• inflammatory bowel disease
• inter-observer agreement
• minimally invasive
• polyps

• capsule endoscopy
• colonoscopy
• colorectal cancer
• inflammatory bowel disease
• polyethylene glycol

The development of artificial intelligence (AI) algorithms has permeated the medical field with great success. The widespread use of AI technology in diagnosing and treating several types of cancer, especially colorectal cancer (CRC), is now attracting substantial attention. CRC, which represents the third most commonly diagnosed malignancy in both men and women, is considered a leading cause of cancer-related deaths globally. Our review herein aims to provide in-depth knowledge and analysis of the AI applications in CRC screening, diagnosis, and treatment based on current literature. We also explore the role of recent advances in AI systems regarding medical diagnosis and therapy, with several promising results. CRC is a highly preventable disease, and AI-assisted techniques in routine screening represent a pivotal step in declining incidence rates of this malignancy. So far, computer-aided detection and characterization systems have been developed to increase the detection rate of adenomas. Furthermore, CRC treatment enters a new era with robotic surgery and novel computer-assisted drug delivery techniques. At the same time, healthcare is rapidly moving toward precision or personalized medicine. Machine learning models have the potential to contribute to individual-based cancer care and transform the future of medicine.

• artificial intelligence
• colonoscopy
• colorectal cancer
• computer-aided detection
• computer-aided diagnosis
• precision oncology
• robotic-assisted surgery
• screening
• therapy

• Adenoma
• Artificial Intelligence
• Colonoscopy
• Colorectal Neoplasms/diagnosis
• Colorectal Neoplasms/epidemiology
• Colorectal Neoplasms/therapy
• Early Detection of Cancer
• Female
• Humans
• Male

• Capsule endoscopy
• Crohn disease
• Ulcerative colitis

• Capsule Endoscopy
• Gastrointestinal Hemorrhage
• Gastrointestinal Transit
• Humans

• Capsule endoscopy
• Cirrhosis
• Enteropathy
• Panenteric
• Portal hypertension
• Surveillance
• Varices

• Capsule Endoscopy
• Endoscopy, Digestive System
• Esophageal and Gastric Varices/etiology
• Humans
• Hypertension, Portal/diagnostic imaging
• Intestine, Small/diagnostic imaging

• Colon capsule endoscopy
• Crohn’s disease
• Diagnostic yield
• Prospective study
• Retention
• Whole gastrointestinal tract

Reliable and especially widely accepted preventive measures are crucial to further reduce the incidence of colorectal cancer (CRC). Colon capsule endoscopy (CCE) might increase the screening numbers among patients unable or unwilling to undergo conventional colonoscopy. This registry trial aimed to document and determine the CCE indications, findings, complications, and adverse events in outpatient practices and clinics throughout Germany.

Patients undergoing CCE between 2010 and 2015 were enrolled in this prospective multicenter registry trial at six German centers. Patient demographics, outcomes, and complications were evaluated.

A total of 161 patients were included. Of the CCE evaluations, 111 (68.9%) were considered successful. Pathological findings in the colon (n=92, 60.1%) and in the remaining gastrointestinal tract (n=38, 24.8%) were recorded. The main finding was the presence of polyps (n=52, 32.3%). Furthermore, five carcinomas (3.1%) were detected and histologically confirmed later. Adequate bowel cleanliness was more likely to be achieved in the outpatient setting (p<0.0001). Interestingly, 85 patients (55.6%) chose to undergo CCE based on personal motivation.

CCE seems to be a reliable and safe endoscopic tool for screening for CRC and detecting other diseases. Its patient acceptance and feasibility seems to be high, especially in the outpatient setting.

• Colonic neoplasms
• Control
• Endoscopy
• Prevention
• Registries
• Safety

• Diagnosis
• Paper
• Paper-based-culture
• Scaffold
• μPAD