Colorectal cancer has a high incidence and mortality rate, and the effectiveness of routine colonoscopy largely depends on the endoscopist’s expertise. In recent years, computer-aided detection (CADe) systems have been increasingly integrated into colonoscopy to improve detection accuracy. However, while most studies have focused on adenoma detection rate (ADR) as the primary outcome, the more sensitive adenoma miss rate (AMR) has been less frequently analyzed.

To evaluate the effectiveness of CADe in colonoscopy and assess the advantages of AMR over ADR.

A comprehensive literature search was conducted in PubMed, Embase, and the Cochrane Central Register of Controlled Trials using predefined search strategies to identify relevant studies published up to August 2, 2024. Statistical analyses were performed to compare outcomes between groups, and potential publication bias was assessed using funnel plots. The quality of the included studies was evaluated using the Cochrane Risk of Bias tool and the Grading of Recommendations, Assessment, Development, and Evaluation approach.

Five studies comprising 1624 patients met the inclusion criteria. AMR was significantly lower in the CADe-assisted group than in the routine colonoscopy group (147/927, 15.9% vs 345/960, 35.9%; P < 0.01). However, CADe did not provide a significant advantage in detecting advanced adenomas or lesions measuring 6-9 mm or ≥ 10 mm. The polyp miss rate (PMR) was also lower in the CADe-assisted group [odds ratio (OR), 0.35; 95% confidence interval (CI): 0.23-0.52; P < 0.01]. While the overall ADR did not differ significantly between groups, the ADR during the first-pass examination was higher in the CADe-assisted group (OR, 1.37; 95%CI: 1.10-1.69; P = 0.004). The level of evidence for the included randomized controlled trials was graded as moderate.

CADe can significantly reduce AMR and PMR while improving ADR during initial detection, demonstrating its potential to enhance colonoscopy performance. These findings highlight the value of CADe in improving the detection of colorectal neoplasms, particularly small and histologically distinct adenomas.

• Artificial intelligence
• Computer-aided detection
• Colonoscopy
• Neoplasms
• Prevention and control

• Artificial intelligence
• Endoscopy
• Laterally spreading tumors

• Humans
• Colorectal Neoplasms/pathology
• Colorectal Neoplasms/diagnosis
• Colorectal Neoplasms/diagnostic imaging
• Neural Networks, Computer
• Algorithms
• Colonoscopy/methods
• Deep Learning

• No.2022A1515011230，2022A1515012315 Natural Science Foundation of Guangdong Province
• Grant No. 23SZD039 Municipal Key Science and Technology Program of Leshan

• Artificial intelligence
• GI Genius
• adenoma detection rate
• colonoscopy
• colorectal cancer screening

• Humans
• Colorectal Neoplasms/diagnosis
• Colonoscopy/methods
• Adenoma/diagnosis
• Adenoma/diagnostic imaging
• Artificial Intelligence

• AI-assisted pathological diagnosis
• CRC (colorectal cancer)
• artificial intelligence (AI)
• pathological diagnosis
• transfer learning

• CADe
• CADx
• artificial intelligence
• colonoscopy

• adenoma
• artificial intelligence
• colon cancer screening
• computer-aided detection
• early detection

• sessile serrated adenoma
• sessile serrated adenoma/polyp
• sessile serrated lesion
• sessile serrated lesion with dysplasia
• sessile serrated polyp

• Adenoma/diagnosis
• Adenoma/genetics
• Adenoma/surgery
• Colonic Polyps/diagnosis
• Colonic Polyps/genetics
• Colonic Polyps/surgery
• Colonoscopy
• Colorectal Neoplasms/pathology
• Humans
• Hyperplasia
• Microsatellite Instability

• colonoscopy
• detection
• education
• flat and depressed lesions
• randomized trial

• Asia
• Colonoscopy/methods
• Colorectal Neoplasms/diagnosis
• Colorectal Neoplasms/pathology
• Humans
• Internet

• 2020/21 Asian Endoscopy Research Forum (AERF)

• artificial intelligence
• colonic polyps
• colonoscopy
• colorectal neoplasms
• deep learning

• Algorithms
• Artificial Intelligence
• Colonic Polyps/diagnosis
• Colonic Polyps/pathology
• Colonoscopy
• Colorectal Neoplasms/diagnosis
• Colorectal Neoplasms/pathology
• Humans

• 203145Z/16/Z Wellcome
• 203145Z/16/Z /EPSRC Centre for Interventional and Surgical Sciences (WEISS) at UCL

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

Computer-aided detection (CADe) systems have been actively researched for polyp detection in colonoscopy. To be an effective system, it is important to detect additional polyps that may be easily missed by endoscopists. Sessile serrated lesions (SSLs) are a precursor to colorectal cancer with a relatively higher miss rate, owing to their flat and subtle morphology. Colonoscopy CADe systems could help endoscopists; however, the current systems exhibit a very low performance for detecting SSLs. We propose a polyp detection system that reflects the morphological characteristics of SSLs to detect unrecognized or easily missed polyps. To develop a well-trained system with imbalanced polyp data, a generative adversarial network (GAN) was used to synthesize high-resolution whole endoscopic images, including SSL. Quantitative and qualitative evaluations on GAN-synthesized images ensure that synthetic images are realistic and include SSL endoscopic features. Moreover, traditional augmentation methods were used to compare the efficacy of the GAN augmentation method. The CADe system augmented with GAN synthesized images showed a 17.5% improvement in sensitivity on SSLs. Consequently, we verified the potential of the GAN to synthesize high-resolution images with endoscopic features and the proposed system was found to be effective in detecting easily missed polyps during a colonoscopy.

• Artificial intelligence
• Deep learning
• Diagnosis
• Digestive system
• Endoscopic
• Imaging
• Pathology

Colorectal cancer is a cancer type that is most suitable for screening since subjects at risk of this malignancy can clearly benefit from colonoscopy screening. In 2017, there were about 431951 new cases of colorectal cancer in China, with an increase of 203.5% in 28 years. Early detection and early removal of adenomatous polyps and other precancerous lesions during colonoscopy can prevent the occurrence of colorectal cancer. However, various factors lead to missed diagnosis of polyps during colonoscopy, which increases the risk of colorectal cancer. In recent years, with the rapid development of artificial intelligence technology in the medical field, colonoscopy assisted by artificial intelligence can increase the detection rate of polyps and improve the quality of colonoscopy. This paper mainly reviews the quality control, bowel preparation, diagnosis and classification of colorectal polyps, and the future opportunities and challenges faced by convolutional neural network based artificial intelligence technology in the field of colonoscopy, hoping to provide some reference for clinical work.

• Artificial intelligence
• Deep learning
• Colonoscopy
• Colorectal polyps
• Adenoma detection rate

Early gastrointestinal (GI) cancer has been the core of clinical endoscopic work. Its early detection and treatment are tightly associated with patients’ prognoses. As a novel technology, artificial intelligence has been improved and applied in the field of endoscopy. Studies on detection, diagnosis, risk, and prognosis evaluation of diseases in the GI tract have been in development, including precancerous lesions, adenoma, early GI cancers, and advanced GI cancers. In this review, research on esophagus, stomach, and colon was concluded, and associated with the process from precancerous lesions to early GI cancer, such as from Barrett’s esophagus to early esophageal cancer, from dysplasia to early gastric cancer, and from adenoma to early colonic cancer. A status quo of research on early GI cancers and artificial intelligence was provided.

• Artificial intelligence
• Early esophageal cancer
• Early gastric cancer
• Early colonic cancer

This study aims to analyze the endoscopic and pathological characteristics of colorectal laterally spreading tumors (LSTs) to assist malignant risk stratification to inform selection of the appropriate treatment strategy.

Patients with colorectal LST were selected as retrospective study objects. Characteristics, including endoscopic findings and the most common site of LSTs of different diameters and histological types, were analyzed. The risk factors for malignancy in colorectal LST were explored by multivariate logistic regression analysis.

LSTs with diameters of ≥20 mm were found mainly in the rectum and mainly with granular-mixed (G-M) morphology (36% and 44.6%, respectively; p < 0.05), while LSTs with diameters of <20 mm were found mainly in the ascending colon and mainly with granular-homogenous (G-H) morphology (40.9% and 46.2%, respectively; p < 0.05). Adenoma was the main histological type in patients with tumors of all diameters. However, the cancerization rate of LSTs was 31% in patients with tumor diameter ≥20 mm, while there was no invasive cancer in patients with tumor diameter < 20 mm. In the low-grade dysphasia (adenoma) group, most of the lesions were located in the ascending colon and most had the morphology LST-G-H (35.8% and 39.2%, respectively; p < 0.05). In the cancerization group, most of the lesions were located in the rectum, with the morphology LST-G-M (51.6% and 67.2%, respectively; p < 0.05), and the diameter was larger than that of the adenoma group (33.84 ± 17.99 mm vs 21.68 ± 8.99 mm).

The rectum was the most common site for an LST with a diameter ≥20 mm and cancerization, of which the morphology was mainly LST-G-M (endoscopic submucosal dissection is the preferred treatment for this type of LST). LST malignancy was found to be correlated with lesion diameter, location, and morphological appearance.

• colonic mucosa
• colonoscopy
• colorectal laterally spreading tumors
• malignant tumor
• pathological features

• CADe
• artificial intelligence
• colonoscopy
• detection
• non-polypoid neoplasia

• artificial intelligence
• colonoscopy
• computer-aided diagnosis
• polyp