The Role of Artificial Intelligence in Colorectal Cancer Screening: Lesion Detection and Lesion Characterization

Article Gastroenterology & Hepatology

Artificial intelligence-assisted optical diagnosis for the resect-and-discard strategy in clinical practice: the Artificial intelligence BLI Characterization (ABC) study

Emanuele Rondonotti et al.

Summary: The present study demonstrates that real-time artificial intelligence (AI)-assisted optical diagnosis is accurate and useful for the diagnosis of diminutive rectosigmoid polyps (DRSPs), especially for nonexperts.

ENDOSCOPY (2023)

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Article Gastroenterology & Hepatology

Computer-aided Detection of Polyps Does Not Improve Colonoscopist Performance in a Pragmatic Implementation Trial

Uri Ladabaum et al.

GASTROENTEROLOGY (2023)

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Article Gastroenterology & Hepatology

Computer-aided diagnosis of early-stage colorectal cancer using nonmagnified endoscopic white-light images (with videos)

Daiki Nemoto et al.

Summary: This study aimed to develop a computer-aided diagnosis (CADx) system using nonmagnified endoscopic white-light images to diagnose early-stage colorectal cancers (CRCs). The CADx system showed high specificity and accuracy for diagnosing T1b lesions, surpassing trainees and comparable to experts. The CADx system has promising potential for clinical application.

GASTROINTESTINAL ENDOSCOPY (2023)

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Article Gastroenterology & Hepatology

Artificial Intelligence-Assisted Colonoscopy for Colorectal Cancer Screening: A Multicenter Randomized Controlled Trial

Hong Xu et al.

Summary: This multicenter randomized controlled trial found that AI-assisted colonoscopy improves polyp detection and characterization in an asymptomatic population. AI-assisted colonoscopy can increase the adenoma detection rate and the detection ability of both expert and non-expert endoscopists.

CLINICAL GASTROENTEROLOGY AND HEPATOLOGY (2023)

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Article Gastroenterology & Hepatology

Artificial intelligence empowers the second-observer strategy for colonoscopy: a randomized clinical trial

Pu Wang et al.

Summary: In colonoscopy screening for colorectal cancer, human vision limitations may lead to higher miss rate of lesions. Artificial intelligence (AI) assistance has been demonstrated to improve polyp detection. This study aimed to compare the effectiveness of AI and human observer during colonoscopy.

GASTROENTEROLOGY REPORT (2023)

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Article Medicine, General & Internal

Assessment of the Role of Artificial Intelligence in the Association Between Time of Day and Colonoscopy Quality

Zihua Lu et al.

Summary: This study aimed to validate whether the assistance of an AI system could overcome the time-related decline in adenoma detection during colonoscopy. Through comparative analysis, the study found that AI systems had higher assistance ability in the late sessions of colonoscopy, suggesting the potential to maintain high quality and homogeneity of colonoscopies and improve endoscopist performance in large screening programs and centers with high workloads.

JAMA NETWORK OPEN (2023)

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Article Gastroenterology & Hepatology

Evaluation of Computer-Aided Detection During Colonoscopy in the Community (AI-SEE): A Multicenter Randomized Clinical Trial

Mike T. Wei et al.

AMERICAN JOURNAL OF GASTROENTEROLOGY (2023)

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Article Gastroenterology & Hepatology

Real-World Validation of a Computer-Aided Diagnosis System for Prediction of Polyp Histology in Colonoscopy: A Prospective Multicenter Study

James Weiquan Li et al.

Summary: Computer-aided diagnosis (CADx) has the potential to support endoscopists in clinical decision making, but its performance in a real-world setting has not been validated. In this prospective, multicenter study, CADx predictions were compared with endoscopist predictions of polyp histology during colonoscopy. The results showed that endoscopists had higher accuracy and sensitivity compared to CADx, with moderate agreement between the two. Concordance between CADx and endoscopist predictions improved diagnostic accuracy. Further research is needed to improve the performance of CADx in clinical practice.

AMERICAN JOURNAL OF GASTROENTEROLOGY (2023)

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Article Gastroenterology & Hepatology

Impact of Artificial Intelligence on Colonoscopy Surveillance After Polyp Removal: A Pooled Analysis of Randomized Trials

Yuichi Mori et al.

Summary: The use of AI during colonoscopy increases the proportion of patients needing intensive surveillance by approximately 35% in the United States and 20% in Europe. This contributes to improved cancer prevention but adds significant patient burden and healthcare costs.

CLINICAL GASTROENTEROLOGY AND HEPATOLOGY (2023)

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Review Gastroenterology & Hepatology

Benefits and challenges in implementation of artificial intelligence in colonoscopy: World Endoscopy Organization position statement

Yuichi Mori et al.

Summary: The number of AI tools for colonoscopy is increasing, but their implementation is facing challenges due to lack of clinical evidence and cost-effectiveness, lack of guidelines, uncertain indications, and implementation costs. The World Endoscopy Organization (WEO) has provided its position statement to address these issues and guide practitioners. The statement includes recommendations for the use of computer-aided detection (CADe) and computer-aided diagnosis (CADx), and emphasizes the need for cost-effectiveness research to understand the benefits of AI implementation.

DIGESTIVE ENDOSCOPY (2023)

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Letter Gastroenterology & Hepatology

Comparative Performance of Artificial Intelligence Optical Diagnosis Systems for Leaving in Situ Colorectal Polyps

Cesare Hassan et al.

GASTROENTEROLOGY (2023)

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Article Gastroenterology & Hepatology

Clinical Evaluation of Computer-Aided Colorectal Neoplasia Detection Using a Novel Endoscopic Artificial Intelligence: A Single-Center Randomized Controlled Trial

Hirotaka Nakashima et al.

Summary: This study evaluated the clinical performance of a new endoscopic artificial intelligence system for computer-aided detection (CADe) of colonic adenomas. The CADe group showed a 11.8% higher adenoma detection rate (ADR) compared to the control group. Additionally, there was no need to extend the examination time or request the assistance of additional medical staff to achieve this improved effectiveness.

DIGESTION (2023)

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Article Gastroenterology & Hepatology

Influence of artificial intelligence on the adenoma detection rate throughout the day

Rino Richter et al.

Summary: The use of artificial intelligence systems can help overcome the decline in adenoma detection rate during the daytime, potentially due to the constant awareness provided by the AI system.

DIGESTIVE DISEASES (2023)

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Article Gastroenterology & Hepatology

Development and validation of an artificial intelligence-based system for predicting colorectal cancer invasion depth using multi-modal data

Liwen Yao et al.

Summary: A clinically applicable artificial intelligence system was constructed to accurately predict the depth of cancer invasion in large sessile colorectal polyps, providing important guidance for treatment strategies.

DIGESTIVE ENDOSCOPY (2023)

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Article Gastroenterology & Hepatology

Performance and attitudes toward real-time computer-aided polyp detection during colonoscopy in a large tertiary referral center in the United States

Fredy Nehme et al.

Summary: This study evaluated the effectiveness of the first FDA-approved CADe device for polyp detection in colonoscopy and investigated the attitudes toward its implementation. The results showed that CADe did not improve adenoma detection in clinical practice, and there were concerns raised by healthcare professionals regarding AI-assisted colonoscopy.

GASTROINTESTINAL ENDOSCOPY (2023)

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Article Gastroenterology & Hepatology

Performance of a novel computer-aided diagnosis system in the characterization of colorectal polyps, and its role in meeting Preservation and Incorporation of Valuable Endoscopic Innovations standards set by the American Society of Gastrointestinal Endoscopy

Ejaz Hossain et al.

Summary: This study validates a novel AI algorithm developed by NEC Corporation for the characterization of colorectal polyps. The algorithm demonstrates higher sensitivity, accuracy, and consistency compared to endoscopists in diagnosing diminutive polyps and predicting post-polypectomy surveillance intervals. AI-based optical diagnosis shows promise in improving the performance of general endoscopists.

DEN OPEN (2023)

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Article Gastroenterology & Hepatology

A computer-aided diagnosis system using white-light endoscopy for the prediction of conventional adenoma with high grade dysplasia

Sijun Meng et al.

Summary: ECRC-CAD is a computer-aided diagnosis system developed using standard white-light endoscopy for predicting conventional adenomas with high-grade dysplasia. It achieved good diagnostic capability for HGD and outperformed expert endoscopists in diagnosing HGD.

DIGESTIVE AND LIVER DISEASE (2022)

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Article Gastroenterology & Hepatology

Polyp detection and false-positive rates by computer-aided analysis of withdrawal-phase videos of colonoscopy of the right-sided colon segment in a randomized controlled trial comparing water exchange and air insufflation

Chia-Pei Tang et al.

Summary: Water exchange improves polyp detection rate and reduces false positive numbers. Computer-aided detection complements water exchange, optimizing polyp detection.

GASTROINTESTINAL ENDOSCOPY (2022)

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Review Gastroenterology & Hepatology

Effect of artificial intelligence-aided colonoscopy for adenoma and polyp detection: a meta-analysis of randomized clinical trials

Ding Huang et al.

Summary: This meta-analysis included 10 randomized controlled trials with 6629 individuals in AI-aided and routine groups, showing that AI-aided endoscopy significantly improved ADR and PDR compared to routine colonoscopy. Lesion detection rates such as APC, PPC, and SSLPC were significantly higher in the AI-aided group, while withdrawal time was longer in the AI-aided group when biopsies were included.

INTERNATIONAL JOURNAL OF COLORECTAL DISEASE (2022)

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Review Biochemistry & Molecular Biology

AI in health and medicine

Pranav Rajpurkar et al.

Summary: This Review discusses the potential of AI to reshape medicine and make healthcare more accurate, efficient, and accessible. It covers recent progress in medical AI, including advances in medical image analysis, non-image data sources, and human-AI collaboration. The article also addresses technical and ethical challenges, such as data scarcity and racial bias.

NATURE MEDICINE (2022)

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Article Gastroenterology & Hepatology

Deep Learning Computer-aided Polyp Detection Reduces Adenoma Miss Rate: A United States Multi-center Randomized Tandem Colonoscopy Study (CADeT-CS Trial)

Jeremy R. Glissen Brown et al.

Summary: In a U.S. multicenter tandem colonoscopy randomized controlled trial, the use of a CADe-system was shown to decrease AMR and SSL miss rate while increasing first-pass APC compared to HDWL colonoscopy alone.

CLINICAL GASTROENTEROLOGY AND HEPATOLOGY (2022)

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Article Gastroenterology & Hepatology

Impact of the clinical use of artificial intelligence-assisted neoplasia detection for colonoscopy: a large-scale prospective propensity score-matched study (with video)

Misaki Ishiyama et al.

Summary: This study found that using CADe system for colonoscopy significantly increased the adenoma detection rate in a large-scale prospective evaluation, while there was no significant increase in the detection rate of advanced neoplasia.

GASTROINTESTINAL ENDOSCOPY (2022)

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Article Gastroenterology & Hepatology

Artificial intelligence and colonoscopy experience: lessons from two randomised trials

Alessandro Repici et al.

Summary: Artificial intelligence assistance during colonoscopy can increase adenoma detection rate (ADR) and related polyp parameters in less experienced examiners compared to the control group. The level of examiner experience does not play a significant role in determining ADR, as shown in this study.

GUT (2022)

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Article Gastroenterology & Hepatology

Efficacy of a computer-aided detection system in a fecal immunochemical test-based organized colorectal cancer screening program: a randomized controlled trial (AIFIT study)

Emanuele Rondonotti et al.

Summary: The use of CADe in a FIT-based CRC screening program significantly increases the adenoma detection rate and the number of adenomas per colonoscopy. The impact of CADe appears to be consistent regardless of the endoscopist's baseline ADR.

ENDOSCOPY (2022)

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Article Gastroenterology & Hepatology

Computer-Aided Detection Improves Adenomas per Colonoscopy for Screening and Surveillance Colonoscopy: A Randomized Trial

Aasma Shaukat et al.

Summary: This study evaluated the clinical benefit and safety of using a computer-aided detection (CADe) device in colonoscopy procedures. The results showed that the use of CADe device significantly increased adenoma detection rate without increasing the resection of non-neoplastic lesions.

GASTROENTEROLOGY (2022)

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Article Gastroenterology & Hepatology

Impact of Artificial Intelligence on Miss Rate of Colorectal Neoplasia

Michael B. Wallace et al.

Summary: This study demonstrates that the use of artificial intelligence can significantly reduce the miss rate of colorectal neoplasia, especially for small and subtle lesions. This is of great importance in improving the prevention of colorectal cancer.

GASTROENTEROLOGY (2022)

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Article Gastroenterology & Hepatology

Real-time, computer-aided, detection-assisted colonoscopy eliminates differences in adenoma detection rate between trainee and experienced endoscopists

Giuseppe Biscaglia et al.

Summary: This study aimed to assess whether AI might eliminate any difference in ADR or AMR between trainee endoscopists and experienced endoscopists. The results showed no significant difference in ADR or AMR between the AI-supported trainee endoscopists and experienced endoscopists.

ENDOSCOPY INTERNATIONAL OPEN (2022)

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Article Gastroenterology & Hepatology

Does computer-aided diagnostic endoscopy improve the detection of commonly missed polyps? A meta-analysis

Arun Sivananthan et al.

Summary: This meta-analysis examines the application of computer-aided detection (CADe) systems in colonoscopy. The results demonstrate that CADe systems can significantly improve the detection rate of commonly missed lesions.

CLINICAL ENDOSCOPY (2022)

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Review Gastroenterology & Hepatology

Mucosal imaging in colon polyps: New advances and what the future may hold

Edward John Young et al.

Summary: An expanding range of advanced mucosal imaging technologies have been developed to improve the detection and characterization of gastrointestinal tract lesions, particularly colorectal neoplasia. Improved adenoma detection reduces miss rates and prevents cancer development without significantly increasing procedural time. Accurate polyp characterization guides resection techniques and the use of different strategies. This review aims to summarize the evidence regarding these technologies and guide colonoscopic practice.

WORLD JOURNAL OF GASTROENTEROLOGY (2022)

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Article Gastroenterology & Hepatology

Robust automated prediction of the revised Vienna Classification in colonoscopy using deep learning: development and initial external validation

Masayoshi Yamada et al.

Summary: An AI system has been developed that can predict pathological diagnosis based on standard colonoscopy images. The system utilizes deep learning algorithms and a large dataset of pathologically proven lesions. It outperforms expert endoscopists in differential diagnosis of colorectal neoplasia during colonoscopy.

JOURNAL OF GASTROENTEROLOGY (2022)

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Article Gastroenterology & Hepatology

Artificial Intelligence-Aided Colonoscopy Does Not Increase Adenoma Detection Rate in Routine Clinical Practice

Idan Levy et al.

Summary: The study compared the effectiveness of AIAC and traditional colonoscopy, and found that the introduction of artificial intelligence did not improve the detection rates, and raised important questions on AI-human interactions in medicine.

AMERICAN JOURNAL OF GASTROENTEROLOGY (2022)

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Article Gastroenterology & Hepatology

Expected value of artificial intelligence in gastrointestinal endoscopy: European Society of Gastrointestinal Endoscopy (ESGE) Position Statement

Helmut Messmann et al.

Summary: The ESGE defines the expected value of AI for the diagnosis and management of gastrointestinal neoplasia and sets recommendations for its acceptance in different aspects of endoscopy.

ENDOSCOPY (2022)

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Article Gastroenterology & Hepatology

Performance and comparison of artificial intelligence and human experts in the detection and classification of colonic polyps

Ming-De Li et al.

Summary: The aim of this study was to analyze the performance of different artificial intelligence models in endoscopic colonic polyp detection and classification and compare them with doctors of different experience. The results showed that the performance of AI group in detecting and classifying colonic polyps was similar to that of the expert group, with high sensitivity and moderate specificity. Different tasks may have an impact on the performance of deep learning models and human experts, especially in terms of sensitivity and specificity.

BMC GASTROENTEROLOGY (2022)

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Article Medical Informatics

Cost-effectiveness of artificial intelligence for screening colonoscopy: a modelling study

Miguel Areia et al.

Summary: The study investigated the impact of implementing AI detection tools in screening colonoscopy on colorectal cancer incidence, mortality, and cost-effectiveness. The results showed that using AI tools during colonoscopy can further reduce the incidence and mortality of colorectal cancer and save costs.

LANCET DIGITAL HEALTH (2022)

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Review Gastroenterology & Hepatology

Artificial intelligence for polyp detection during colonoscopy: a systematic review and meta-analysis

Ishita Barua et al.

Summary: Through analysis of five randomized trials, it was found that the introduction of AI systems in colonoscopy can increase the detection rates of polyps and adenomas, but has no significant effect on the detection rate of advanced adenomas.

ENDOSCOPY (2021)

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Review Gastroenterology & Hepatology

Performance of artificial intelligence in colonoscopy for adenoma and polyp detection: a systematic review and meta-analysis

Cesare Hassan et al.

Summary: The meta-analysis found that incorporating artificial intelligence as an aid for detecting colorectal neoplasia significantly increases the detection rate of colorectal neoplasia, independently of the main adenoma characteristics.

GASTROINTESTINAL ENDOSCOPY (2021)

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Article Gastroenterology & Hepatology

Factors Associated with Fibrosis during Colorectal Endoscopic Submucosal Dissection: Does Pretreatment Biopsy Potentially Elicit Submucosal Fibrosis and Affect Endoscopic Submucosal Dissection Outcomes?

Masatake Kuroha et al.

Summary: This study found that pretreatment biopsy can lead to severe fibrosis during colorectal ESD, resulting in prolonged procedure times and incomplete resection.

DIGESTION (2021)

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Review Gastroenterology & Hepatology

Artificial intelligence can increase the detection rate of colorectal polyps and adenomas: a systematic review and meta-analysis

Jianglei Li et al.

Summary: This study found that artificial intelligence improves polyp and adenoma detection rates in colonoscopy, and better bowel preparation and training for detecting small polyps and adenomas can also enhance detection rates.

EUROPEAN JOURNAL OF GASTROENTEROLOGY & HEPATOLOGY (2021)

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Article Gastroenterology & Hepatology

Artificial intelligence-assisted colonic endocytoscopy for cancer recognition: a multicenter study

Yuichi Mori et al.

Summary: An artificial intelligence tool was developed to identify colorectal cancer pathology with high specificity using endocytoscopic images, achieving an overall accuracy of 91.9% in differentiating cancer during validation testing.

ENDOSCOPY INTERNATIONAL OPEN (2021)

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Article Gastroenterology & Hepatology

Artificial Intelligence-Assisted Colonoscopy for Detection of Colon Polyps: a Prospective, Randomized Cohort Study

Yuchen Luo et al.

Summary: This study examined whether a high-performance, real-time automatic polyp detection system could improve the polyp detection rate in the actual clinical setting. Results showed that the AI system significantly increased the PDR and detection of smaller polyps, indicating potential for improving early detection of colorectal cancer. However, further research with a larger sample size is needed to confirm these findings.

JOURNAL OF GASTROINTESTINAL SURGERY (2021)

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Review Surgery

Artificial Intelligence-Aided Colonoscopy for Polyp Detection: A Systematic Review and Meta-Analysis of Randomized Clinical Trials

Yuanchuan Zhang et al.

Summary: The study showed that AI-aided colonoscopy significantly improved the polyp detection rate and adenoma detection rate, especially for smaller polyps. However, further improvement is needed for the shape and pathology recognition of the AI technique.

JOURNAL OF LAPAROENDOSCOPIC & ADVANCED SURGICAL TECHNIQUES (2021)

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Review Multidisciplinary Sciences

Comparison of diagnostic performance between convolutional neural networks and human endoscopists for diagnosis of colorectal polyp: A systematic review and meta-analysis

Yixin Xu et al.

Summary: This study found through meta-analysis that the CNN system based on artificial intelligence technology has satisfactory diagnostic performance in detecting and classifying colorectal polyps, showing comparability with experts and superiority over non-experts.

PLOS ONE (2021)

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Article Multidisciplinary Sciences

Prediction of the histology of colorectal neoplasm in white light colonoscopic images using deep learning algorithms

Seong Ji Choi et al.

Summary: By training convolutional neural networks (CNNs) and developing a computer-aided diagnostic (CAD) system, the research team successfully predicted the pathologic histology of colorectal adenoma. The CNN-CAD system showed similar performance to endoscopic experts and outperformed trainees in the study.

SCIENTIFIC REPORTS (2021)

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Article Oncology

Feedforward Artificial Neural Network-Based Colorectal Cancer Detection Using Hyperspectral Imaging: A Step towards Automatic Optical Biopsy

Boris Jansen-Winkeln et al.

Summary: Detection of colorectal carcinoma using hyperspectral imaging and neural networks allows for accurate differentiation between tumor and healthy mucosa, with high reliability. Additionally, significant differences in perfusion parameters related to tumor staging and therapy were observed.

CANCERS (2021)

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Article Gastroenterology & Hepatology

Artificial intelligence-enhanced white-light colonoscopy with attention guidance predicts colorectal cancer invasion depth

Xiaobei Luo et al.

Summary: An AI-enhanced WLC system has been developed to differentiate noninvasive or superficial submucosal invasive neoplasms from deeply invasive CRC with high accuracy, sensitivity, and specificity.

GASTROINTESTINAL ENDOSCOPY (2021)

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Review Gastroenterology & Hepatology

Artificial intelligence (AI) real-time detection vs. routine colonoscopy for colorectal neoplasia: a meta-analysis and trial sequential analysis

Smit S. Deliwala et al.

Summary: This systematic review and meta-analysis evaluated the efficacy of AI-assisted colonoscopies against routine colonoscopies, finding that AI outperformed RC in detecting adenomas and polyps, but was less effective in detecting pedunculated polyps. The findings suggest that AI-assisted colonoscopy could bridge critical gaps in CRC identification.

INTERNATIONAL JOURNAL OF COLORECTAL DISEASE (2021)

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Review Gastroenterology & Hepatology

Computer-aided detection versus advanced imaging for detection of colorectal neoplasia: a systematic review and network meta-analysis

Marco Spadaccini et al.

Summary: Based on systematic review and network meta-analysis, it was found that CADe has a higher detection rate for colorectal neoplasia compared to other techniques (such as chromoendoscopy or tools that increase mucosal visualization), supporting greater incorporation of CADe strategies into community endoscopy services.

LANCET GASTROENTEROLOGY & HEPATOLOGY (2021)

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Article Gastroenterology & Hepatology

An analysis about the function of a new artificial intelligence, CAD EYE with the lesion recognition and diagnosis for colorectal polyps in clinical practice

Naohisa Yoshida et al.

Summary: The study evaluated the functionality of CAD EYE in recognizing and diagnosing colorectal polyp lesions. Results showed that CAD EYE had high detection rates under both normal and high-speed observation, and also had high accuracy in distinguishing neoplastic and hyperplastic lesions.

INTERNATIONAL JOURNAL OF COLORECTAL DISEASE (2021)

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Article Gastroenterology & Hepatology

Reducing adenoma miss rate of colonoscopy assisted by artificial intelligence: a multicenter randomized controlled trial

Shunsuke Kamba et al.

Summary: In a multicenter randomized controlled trial, it was found that the adenoma miss rate could be significantly reduced with CADe assistance based on deep learning, leading to a higher adenoma detection rate compared to standard colonoscopy.

JOURNAL OF GASTROENTEROLOGY (2021)

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Review Health Care Sciences & Services

Diagnostic Accuracy of Artificial Intelligence and Computer-Aided Diagnosis for the Detection and Characterization of Colorectal Polyps: Systematic Review and Meta-analysis

Scarlet Nazarian et al.

Summary: Artificial intelligence technologies show great potential in improving the detection and characterization of colorectal polyps, ultimately reducing the incidence of colorectal cancer. The current generation of AI-based systems demonstrate impressive accuracy in detecting and characterizing colorectal polyps.

JOURNAL OF MEDICAL INTERNET RESEARCH (2021)

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Article Oncology

Colorectal cancer incidence, mortality, and stage distribution in European countries in the colorectal cancer screening era: an international population-based study

Rafael Cardoso et al.

Summary: There are diverse trends in colorectal cancer incidence, mortality, and stage distribution across European countries, mainly due to variations in the implementation of colorectal cancer screening programs.

LANCET ONCOLOGY (2021)

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Article Oncology

Artificial intelligence-assisted colonoscopy: A prospective, multicenter, randomized controlled trial of polyp detection

Lei Xu et al.

Summary: AI-assisted colonoscopy can increase the detection of easily missed polyps, but has a limited impact on the overall polyp detection rate.

CANCER MEDICINE (2021)

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Review Gastroenterology & Hepatology

Impact of real-time use of artificial intelligence in improving adenoma detection during colonoscopy: A systematic review and meta-analysis

Munish Ashat et al.

Summary: This study found that AI-assisted colonoscopy (AIAC) significantly increases the detection rates of adenomas and polyps. Compared to conventional colonoscopy (CC), AIAC shows higher efficacy in detecting adenomas and polyps.

ENDOSCOPY INTERNATIONAL OPEN (2021)

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Article Gastroenterology & Hepatology

Benchmarking definitions of false-positive alerts during computer-aided polyp detection in colonoscopy

Erik A. Holzwanger et al.

Summary: The study investigated the impact of different threshold definitions for false-positive alerts on the diagnostic performance of computer-aided colon polyp detection (CADe) systems. Results demonstrated that the threshold definition of false positives significantly affects the diagnostic performance of CADe.

ENDOSCOPY (2021)

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Article Gastroenterology & Hepatology

Artificial Intelligence-assisted System Improves Endoscopic Identification of Colorectal Neoplasms

Shin-ei Kudo et al.

CLINICAL GASTROENTEROLOGY AND HEPATOLOGY (2020)

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Article Gastroenterology & Hepatology

Prediction of Polyp Pathology Using Convolutional Neural Networks Achieves Resect and Discard Thresholds

Robin Zachariah et al.

AMERICAN JOURNAL OF GASTROENTEROLOGY (2020)

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Editorial Material Gastroenterology & Hepatology

Use of Endoscopic Impression, Artificial Intelligence, and Pathologist Interpretation to Resolve Discrepancies Between Endoscopy and Pathology Analyses of Diminutive Colorectal Polyps

Neal Shahidi et al.

GASTROENTEROLOGY (2020)

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Article Gastroenterology & Hepatology

Improved Accuracy in Optical Diagnosis of Colorectal Polyps Using Convolutional Neural Networks with Visual Explanations

Eun Hyo Jin et al.

GASTROENTEROLOGY (2020)

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Review Gastroenterology & Hepatology

Accuracy of artificial intelligence on histology prediction and detection of colorectal polyps: a systematic review and meta-analysis

Thomas K. L. Lui et al.

GASTROINTESTINAL ENDOSCOPY (2020)

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Article Multidisciplinary Sciences

Endoscopic diagnosis and treatment planning for colorectal polyps using a deep-learning model

Eun Mi Song et al.

SCIENTIFIC REPORTS (2020)

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Article Gastroenterology & Hepatology

Efficacy of Real-Time Computer-Aided Detection of Colorectal Neoplasia in a Randomized Trial

Alessandro Repici et al.

GASTROENTEROLOGY (2020)

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Review Gastroenterology & Hepatology

The impact of deep convolutional neural network-based artificial intelligence on colonoscopy outcomes: A systematic review with meta-analysis

Muhammad Aziz et al.

JOURNAL OF GASTROENTEROLOGY AND HEPATOLOGY (2020)

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Article Medicine, General & Internal

Automated Classification of Colorectal Neoplasms in White-Light Colonoscopy Images via Deep Learning

Young Joo Yang et al.

JOURNAL OF CLINICAL MEDICINE (2020)

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Article Gastroenterology & Hepatology

Computer-aided detection-assisted colonoscopy: classification and relevance of false positives

Cesare Hassan et al.

GASTROINTESTINAL ENDOSCOPY (2020)

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Article Gastroenterology & Hepatology

The single-monitor trial: an embedded CADe system increased adenoma detection during colonoscopy: a prospective randomized study

Peixi Liu et al.

THERAPEUTIC ADVANCES IN GASTROENTEROLOGY (2020)

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Article Gastroenterology & Hepatology

Automated endoscopic detection and classification of colorectal polyps using convolutional neural networks

Tsuyoshi Ozawa et al.

THERAPEUTIC ADVANCES IN GASTROENTEROLOGY (2020)

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Article Gastroenterology & Hepatology

Study on detection rate of polyps and adenomas in artificial-intelligence-aided colonoscopy

Wen-Na Liu et al.

SAUDI JOURNAL OF GASTROENTEROLOGY (2020)

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Article Gastroenterology & Hepatology

Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model

Michael F. Byrne et al.

GUT (2019)

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Article Gastroenterology & Hepatology

Disagreement between high confidence endoscopic adenoma prediction and histopathological diagnosis in colonic lesions ≤3 mm in size

Prasanna Ponugoti et al.

ENDOSCOPY (2019)

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Review Gastroenterology & Hepatology

Magnitude, Risk Factors, and Factors Associated With Adenoma Miss Rate of Tandem Colonoscopy: A Systematic Review and Meta-analysis

Shengbing Zhao et al.

GASTROENTEROLOGY (2019)

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Article Gastroenterology & Hepatology

Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study

Pu Wang et al.

GUT (2019)

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Review Gastroenterology & Hepatology

Overview of Deep Learning in Gastrointestinal Endoscopy

Jun Ki Min et al.

GUT AND LIVER (2019)

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Article Medicine, General & Internal

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