• Artificial intelligence
• Computer vision
• Gastrointestinal endoscopy
• Machine learning
• Natural language processing

• Humans
• Artificial Intelligence
• Endoscopy, Gastrointestinal/methods
• Gastrointestinal Diseases/diagnosis
• Algorithms

• early-stage cancer
• endoscopy
• imaging
• label-free
• optical
• spectroscopy

• Humans
• Neoplasms/diagnostic imaging
• Neoplasms/diagnosis
• Endoscopy
• Optical Imaging
• Early Detection of Cancer
• Animals

• Artificial intelligence
• Colonoscopy
• Computer-assisted diagnosis

• CADe
• CADx
• artificial intelligence
• colonoscopy
• colorectal cancer
• prospect

• Humans
• Artificial Intelligence
• Early Detection of Cancer
• Colonoscopy
• Colorectal Neoplasms/diagnosis
• Colorectal Neoplasms/epidemiology
• Incidence

• artificial intelligence
• colonoscopy
• colorectal cancer
• colorectal polyps
• machine learning

• ADR
• Artificial Intelligence
• CAD
• Colonoscopy
• Endoscopy

• Humans
• Artificial Intelligence
• Colonoscopy/methods
• Colorectal Neoplasms/diagnosis
• Colorectal Neoplasms/prevention & control
• Early Detection of Cancer/methods
• Diagnosis, Computer-Assisted/methods

• Artificial intelligence
• Colorectal cancers
• Intestinal carcinoma
• Machine learning
• Malignant
• Polyps

• CADe
• CADx
• artificial intelligence
• colonoscopy

The early endoscopic identification, resection, and treatment of precancerous adenoma and early-stage cancer has been shown to reduce not only the prevalence of colorectal cancer but also its mortality rate. Recent advances in endoscopic devices and imaging technology have dramatically improved our ability to detect colorectal lesions and predict their pathological diagnosis. In addition to this, rapid advances in artificial intelligence (AI) technology mean that AI-related research and development is now progressing in the diagnostic imaging field, particularly colonoscopy, and AIs (i.e., devices that mimic cognitive abilities, such as learning and problem-solving) already approved as medical devices are now being introduced into everyday clinical practice. Today, there is an increasing expectation that sophisticated AIs will be able to provide high-level diagnostic performance irrespective of the level of skill of the endoscopist. In this paper, we review colonoscopy-related AI research and the AIs that have already been approved and discuss the future prospects of this technology.

Artificial intelligence (AI) is a quickly expanding field in gastrointestinal endoscopy. Although there are a myriad of applications of AI ranging from identification of bleeding to predicting outcomes in patients with inflammatory bowel disease, a great deal of research has focused on the identification and classification of gastrointestinal malignancies. Several of the initial randomized, prospective trials utilizing AI in clinical medicine have centered on polyp detection during screening colonoscopy. In addition to work focused on colorectal cancer, AI systems have also been applied to gastric, esophageal, pancreatic, and liver cancers. Despite promising results in initial studies, the generalizability of most of these AI systems have not yet been evaluated. In this article we review recent developments in the field of AI applied to gastrointestinal oncology.

• Artificial intelligence
• Oncology
• Gastroenterology
• Endoscopy
• Machine learning
• Computer-assisted decision making
• Computer-aided detection
• Computer-aided diagnosis

• Artificial intelligence
• Classification
• Computer-assisted diagnosis
• Deep learning
• Detection
• Endoscopy

Artificial intelligence has become an increasingly hot topic in the last several years, and it has also gained its way into the medical field. In recent years, the application of artificial intelligence in the gastroenterology field has been of increasing interest, particularly in the colonoscopy setting. Novel technologies such as deep neural networks have enabled real‐time computer‐aided polyp detection and diagnosis during colonoscopy. This might lead to increased performance of endoscopists as well as potentially reducing the costs of unnecessary polypectomies of hyperplastic polyps. Newly published prospective trials studying computer‐aided detection showed that the assistance of artificial intelligence significantly increased the detection of polyps and non‐advanced adenomas approximately by 10%, while three tandem randomized control trials proved that the adenoma miss rate was significantly reduced (e.g., 13.8% vs. 36.7% in one Japanese multicenter trial). Promising results have also been shown in prospective single‐arm trials on computer‐aided polyp diagnosis, but the evidence is insufficient to reach a conclusion.

• colorectal cancer
• colorectal polyps

• artificial intelligence
• colorectal cancer
• liquid biopsy
• patients stratification
• robotic surgery

• Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii

• fluorescence molecular endoscopy
• near-infrared
• phantoms
• standardization

• Endoscopy, Gastrointestinal
• Fluorescence
• Phantoms, Imaging
• Quality Control
• Reference Standards

• Artificial intelligence
• Colonic polyps
• Colonoscopy
• Colorectal neoplasm
• Machine learning
• Spectroscopy

• Adenoma
• Artificial intelligence
• Colonoscopy
• Computational intelligence
• Endoscopy
• Surveillance

• Adenoma/diagnosis
• Artificial Intelligence
• Colonic Polyps/surgery
• Colonoscopy
• Colorectal Neoplasms/diagnosis
• Humans

• Computer-aided diagnosis (CAD)
• colon cancer
• polyp

The Workgroup Serrated Polyps and Polyposis (WASP) developed criteria for optical diagnosis of colorectal polyps. The aims of this study were: (1) to improve optical diagnosis of diminutive colorectal polyps, especially SSLs, after training endoscopists in applying WASP criteria on videos of polyps obtained with iScan and (2) to evaluate if the WASP criteria are still useful when polyps are pathologically revised according to the World Health Organization (WHO) 2019 criteria.

Twenty‐one endoscopists participated in a training session and predicted polyp histology on 30 videos of diminutive polyps, before and after training (T₀ and T₁). After three months, they scored another 30 videos (T₂). Primary outcome was overall diagnostic accuracy (DA) at T₀, T₁ and T₂. Polyps were histopathologically classified according to the WHO 2010 and 2019 criteria.

Overall DA (both diminutive adenomas and SSLs) significantly improved from 0.58 (95% CI 0.55–0.62) at T₀ to 0.63 (95% CI 0.60–0.66, p = 0.004) at T₁. For SSLs, DA did not change with 0.51 (95% CI 0.46–0.56) at T₀ and 0.55 (95% CI 0.49–0.60, p = 0.119) at T₁. After three months, overall DA was 0.58 (95% CI 0.54–0.62, p = 0.787, relative to T₀) while DA for SSLs was 0.48 (95% CI 0.42–0.55, p = 0.520) at T₂. After pathological revision according to the WHO 2019 criteria, DA of all polyps significantly changed at all time points.

A training session in applying WASP criteria on endoscopic videos made with iScan did not improve endoscopists' long‐term ability to optically diagnose diminutive polyps. The change of DA following polyp revision according to the revised WHO 2019 criteria suggests that the WASP classification may need revision.

• WASP classification
• adenoma
• colorectal polyp
• diagnostic accuracy
• endoscopist training
• endoscopy
• hyperplastic polyp
• iScan
• optical diagnosis
• sessile serrated lesion

Colorectal cancer remains a leading cause of morbidity and mortality in the United States. Advances in artificial intelligence (AI), specifically computer aided detection and computer-aided diagnosis offer promising methods of increasing adenoma detection rates with the goal of removing more pre-cancerous polyps. Conversely, these methods also may allow for smaller non-cancerous lesions to be diagnosed in vivo and left in place, decreasing the risks that come with unnecessary polypectomies. This review will provide an overview of current advances in the use of AI in colonoscopy to aid in polyp detection and characterization as well as areas of developing research.

• Colonoscopy
• Artificial intelligence
• Computer-aided detection
• Detection
• Characterization
• Computer-aided diagnosis

• Adenoma
• Artificial Intelligence
• Colonic Polyps/diagnostic imaging
• Colonoscopy
• Colorectal Neoplasms/diagnostic imaging
• Humans

Endoscpists always have tried to pursue a perfect colonoscopy, and application of artificial intelligence (AI) using deep-learning algorithms is one of the promising supportive options for detection and characterization of colorectal polyps during colonoscopy. Many retrospective studies conducted with real-time application of AI using convolutional neural networks have shown improved colorectal polyp detection. Moreover, a recent randomized clinical trial reported additional polyp detection with shorter analysis time. Studies conducted regarding polyp characterization provided additional promising results. Application of AI with narrow band imaging in real-time prediction of the pathology of diminutive polyps resulted in high diagnostic accuracy. In addition, application of AI with endocytoscopy or confocal laser endomicroscopy was investigated for real-time cellular diagnosis, and the diagnostic accuracy of some studies was comparable to that of pathologists. With AI technology, we can expect a higher polyp detection rate with reduced time and cost by avoiding unnecessary procedures, resulting in enhanced colonoscopy efficiency. However, for AI application in actual daily clinical practice, more prospective studies with minimized selection bias, consensus on standardized utilization, and regulatory approval are needed. (Gut Liver 2021;15:-353)

• Artificial intelligence
• Colon
• Colonoscopy
• Convolutional neural network
• Polyp

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

• ADR, adenoma detection rate
• AI, artificial intelligence
• AMR, adenoma miss rate
• ANN, artificial neural network
• BE, Barrett’s esophagus
• CAD, computer-aided diagnosis
• CADe, CAD studies for colon polyp detection
• CADx, CAD studies for colon polyp classification
• CI, confidence interval
• CNN, convolutional neural network
• CRC, colorectal cancer
• DL, deep learning
• GI, gastroenterology
• HD-WLE, high-definition white light endoscopy
• HDWL, high-definition white light
• ML, machine learning
• NBI, narrow-band imaging
• NPV, negative predictive value
• PIVI, preservation and Incorporation of Valuable Endoscopic Innovations
• SVM, support vector machine
• VLE, volumetric laser endomicroscopy
• WCE, wireless capsule endoscopy
• WL, white light

• Adenoma detection rate
• Artificial intelligence
• Colonoscopy
• Computer-aided detection and diagnosis
• Endoscopy
• Pathology

• Adenoma/diagnostic imaging
• Artificial Intelligence
• Colonic Polyps
• Colonoscopy
• Colorectal Neoplasms/diagnostic imaging
• Diagnosis, Computer-Assisted
• Humans
• Pathologists

• artificial intelligence
• colon cancer
• colonoscopy
• colorectal cancer
• computer-aided diagnosis
• polyp
• screening

• Artificial intelligence
• Colonoscopy
• Value-based care

• Adenoma/diagnosis
• Artificial Intelligence
• Colonoscopy/methods
• Colorectal Neoplasms/diagnosis
• Diagnosis, Computer-Assisted
• Early Detection of Cancer
• Humans
• Intestinal Polyps/diagnosis

• T32 DK007017 NIDDK NIH HHS

• Computer-assisted diagnosis
• endoscopic imaging
• optical diagnosis
• robotic endoscopy

• autofluorescence imaging
• clinical studies
• endogenous fluorophores
• imaging
• inflammation
• systematic review

Artificial intelligence (AI) is rapidly integrating into modern technology and clinical practice. Although in its nascency, AI has become a hot topic of investigation for applications in clinical practice. Multiple fields of medicine have embraced the possibility of a future with AI assisting in diagnosis and pathology applications.

In the field of gastroenterology, AI has been studied as a tool to assist in risk stratification, diagnosis, and pathologic identification. Specifically, AI has become of great interest in endoscopy as a technology with substantial potential to revolutionize the practice of a modern gastroenterologist. From cancer screening to automated report generation, AI has touched upon all aspects of modern endoscopy.

Here, we review landmark AI developments in endoscopy. Starting with broad definitions to develop understanding, we will summarize the current state of AI research and its potential applications. With innovation developing rapidly, this article touches upon the remarkable advances in AI-assisted endoscopy since its initial evaluation at the turn of the millennium, and the potential impact these AI models may have on the modern clinical practice. As with any discussion of new technology, its limitations must also be understood to apply clinical AI tools successfully.

• Artificial intelligence
• Colonoscopy
• Computer assisted diagnosis
• Early detection of cancer
• Endoscopy

• Artificial intelligence
• Colon polyp
• Colonoscopia
• Colonoscopy
• Computer-aided diagnosis
• Detección de pólipos
• Diagnóstico asistido por computador
• Histological prediction
• Inteligencia artificial
• Machine learning
• Polyp detection
• Predicción histológica
• Pólipo de colon

• AI
• endoscopy
• imaging

• Colonoscopia
• Colonoscopy
• Colorectal cancer
• Cáncer colorrectal
• Diagnóstico óptico
• Optical diagnosis
• Polyp
• Pólipo

• Colonic Polyps/diagnostic imaging
• Colonoscopes/standards
• Colonoscopy/instrumentation
• Colonoscopy/methods
• Colorectal Neoplasms/diagnostic imaging
• Equipment Design
• Humans
• Intestinal Polyps/classification
• Intestinal Polyps/diagnostic imaging
• Precancerous Conditions/diagnostic imaging
• Rectal Diseases/diagnostic imaging

• Adenoma/pathology
• Adult
• Aged
• Colonic Neoplasms/pathology
• Colonic Polyps/pathology
• Colonoscopy/instrumentation
• Computer Terminals
• Equipment Design
• Female
• Humans
• Male
• Middle Aged
• Prospective Studies

• Adenocarcinoma/diagnosis
• Adenocarcinoma/pathology
• Adenocarcinoma/prevention & control
• Adenocarcinoma/surgery
• Adenoma/diagnosis
• Adenoma/pathology
• Adenoma/surgery
• Artificial Intelligence
• Colonic Polyps/diagnosis
• Colonic Polyps/pathology
• Colonic Polyps/surgery
• Colonoscopy
• Colorectal Neoplasms/diagnosis
• Colorectal Neoplasms/pathology
• Colorectal Neoplasms/surgery
• Deep Learning
• Diagnosis, Computer-Assisted
• Humans
• Intestinal Polyps/diagnosis
• Intestinal Polyps/pathology
• Intestinal Polyps/surgery
• Quality Assurance, Health Care

• computer-aided
• detection
• esophagus
• gastroscopy
• stomach

• automated
• characterization
• colon
• detection

• Computer-aided diagnosis
• auto-fluorescence imaging
• diminutive
• negative predictive value
• polyp differentiation

We developed a computer-aided diagnosis (CAD) system based on linked color imaging (LCI) images to predict the histological results of polyps by analyzing the colors of the lesions. A total of 139 images of adenomatous polyps and 69 images of non-adenomatous polyps obtained from our hospital were collected and used to train the CAD system. A test set of LCI images, including both adenomatous and non-adenomatous polyps, was prospectively collected from patients who underwent colonoscopies between Oct and Dec 2017; this test set was used to assess the diagnostic abilities of the CAD system compared to those of human endoscopists (two experts and two novices). The accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of this novel CAD system for the training set were 87.0%, 87.1%, 87.0%, 93.1%, and 76.9%, respectively. The test set included 115 adenomatous polyps and 66 non-adenomatous polyps that were prospectively collected. The CAD system identified adenomatous or non-adenomatous polyps in the test set with an accuracy of 78.4%, a sensitivity of 83.3%, a specificity of 70.1%, a PPV of 82.6%, and an NPV of 71.2%. The accuracy of the CAD system was comparable to that of the expert endoscopists (78.4% vs 79.6%; p = 0.517). In addition, the diagnostic accuracy of the novices was significantly lower to the performance of the experts (70.7% vs 79.6%; p = 0.018). A novel CAD system based on LCI could be a rapid and powerful decision-making tool for endoscopists.

• Autofluorescence endoscopy
• Colorectal cancer
• Histopatology report
• Numerical colour value

• adenoma
• autofluorescence imaging
• colon
• color
• polyp

The major role of colonoscopy with polypectomy in reducing the incidence of and mortality from colorectal cancer has been firmly established. Yet there is cause to be uneasy. One of the most striking recent findings is that there is an alarmingly high incomplete polyp removal rate. This phenomenon, together with missed polyps during screening colonoscopy, is thought to be responsible for the majority of interval cancers. Knowledge of serrated polyps needs to broaden as well, since they are quite often missed or incompletely removed. Removal of small and diminutive polyps is almost devoid of complications. Cold snare polypectomy seems to be the best approach for these lesions, with biopsy forcep removal reserved only for the tiniest of polyps. Hot snare or hot biopsy forcep removal of these lesions is no longer recommended. Endoscopic mucosal resection and endoscopic submucosal dissection have proven to be effective in the removal of large colorectal lesions, avoiding surgery in the majority of patients, with acceptably low complication rates. Variants of these approaches, as well as new hybrid techniques, are being currently tested. In this paper, we review the current status of the different approaches in removing polypoid and nonpolypoid lesions of the colon, their complications, and future directions in the prevention of colorectal cancer.

• adenoma
• bleeding
• cold snare polypectomy
• colonoscopy
• colorectal cancer
• endoscopic resection
• mucosal
• perforation
• serrated polyps
• submucosal

• autofluorescence imaging
• colonoscopy
• colorectal cancer
• diminutive
• polyp

AIM: To evaluate the efficacy of computer-assisted color analysis of colorectal lesions using a novel auto-fluorescence imaging (AFI) system to distinguish neoplastic lesions from non-neoplastic lesions and to predict the depth of invasion.

METHODS: From January 2013 to April 2013, consecutive patients with known polyps greater than 5 mm in size who were scheduled to undergo endoscopic treatment at The Jikei University Hospital were prospectively recruited for this study. All lesions were evaluated using a novel AFI system, and color-tone sampling was performed in a region of interest determined from narrow band imaging or from chromoendoscopy findings without magnification. The green/red (G/R) ratio for each lesion on the AFI images was calculated automatically using a computer-assisted color analysis system that permits real-time color analysis during endoscopic procedures.

RESULTS: A total of 88 patients with 163 lesions were enrolled in this study. There were significant differences in the G/R ratios of hyperplastic polyps (non-neoplastic lesions), adenoma/intramucosal cancer/submucosal (SM) superficial cancer, and SM deep cancer (P < 0.0001). The mean ± SD G/R ratios were 0.984 ± 0.118 in hyperplastic polyps and 0.827 ± 0.081 in neoplastic lesions. The G/R ratios of hyperplastic polyps were significantly higher than those of neoplastic lesions (P < 0.001). When a G/R ratio cut-off value of > 0.89 was applied to determine non-neoplastic lesions, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were 83.9%, 82.6%, 53.1%, 95.6% and 82.8%, respectively. For neoplastic lesions, the mean G/R ratio was 0.834 ± 0.080 in adenoma/intramucosal cancer/SM superficial cancer and 0.746 ± 0.045 in SM deep cancer. The G/R ratio of adenoma/intramucosal cancer/SM superficial cancer was significantly higher than that of SM deep cancer (P < 0.01). When a G/R ratio cut-off value of < 0.77 was applied to distinguish SM deep cancers, the sensitivity, specificity, PPV, NPV, and accuracy were 80.0%, 84.4%, 29.6%, 98.1% and 84.1%, respectively.

CONCLUSION: The novel AFI system with color analysis was effective in distinguishing non-neoplastic lesions from neoplastic lesions and might allow determination of the depth of invasion.

• Autofluorescence imaging
• Computer-aided diagnosis
• Colon
• Neoplasia
• Colonoscopy

• Adenoma/pathology
• Aged
• Analysis of Variance
• Area Under Curve
• Colonic Polyps/pathology
• Colonoscopy/methods
• Color
• Colorectal Neoplasms/pathology
• Diagnosis, Differential
• Female
• Humans
• Hyperplasia
• Image Interpretation, Computer-Assisted
• Japan
• Male
• Middle Aged
• Neoplasm Invasiveness
• Optical Imaging/methods
• Predictive Value of Tests
• Prognosis
• Prospective Studies
• ROC Curve