An overview of deep learning algorithms and water exchange in colonoscopy in improving adenoma detection

Factors affecting cecal intubation time during colonoscopy

In recent years, the morbidity and mortality of colorectal cancer (CRC) have increased significantly in China, and it has become one of the major malignancies that threaten the health of residents. Colonoscopy is the gold standard for the diagnosis of CRC. High-quality colonoscopy can effectively reduce the mortality of CRC. Cecal intubation time (CIT) is one of the indicators to reflect the quality of colonoscopy. Studies have found that many factors can affect CIT. This paper reviews the literature to identify the factors that affect CIT, such as those related to doctors, patients, assistive technologies, and equipment, in order to improve the quality of colonoscopy performed by endoscopists.

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

BACKGROUND AND AIMS

Water exchange (WE) improves lesion detection but misses polyps because of human limitations. Computer-aided detection (CADe) identifies additional polyps overlooked by the colonoscopist. Additional polyp detection rate (APDR) is the proportion of patients with at least 1 additional polyp detected by CADe. The number of false positives (because of feces and air bubble) per colonoscopy (FPPC) is a major CADe limitation, which might be reduced by salvage cleaning with WE. We compared the APDR and FPPC by CADe between videos of WE and air insufflation in the right-sided colon.

METHODS

CADe used a convolutional neural network with transfer learning. We edited and coded withdrawal-phase videos in a randomized controlled trial that compared right-sided colon findings between air insufflation and WE. Two experienced blinded endoscopists analyzed the CADe-overlaid videos and identified additional polyps by consensus. An artifact triggered by CADe but not considered a polyp by the reviewers was defined as a false positive. The primary outcome was APDR.

RESULTS

Two hundred forty-five coded videos of colonoscopies inserted with WE (n = 123) and air insufflation (n = 122) methods were analyzed. The APDR in the WE group was significantly higher (37 [30.1%] vs 15 [12.3%], P = .001). The mean [standard deviation] FPPC related to feces (1.78 [1.67] vs 2.09 [2.09], P = .007) and bubbles (.53 [.89] vs 1.25 [2.45], P = .001) in the WE group were significantly lower.

CONCLUSIONS

CADe showed significantly higher APDR and lower number of FPPC related to feces and bubbles in the WE group. The results support the hypothesis that the strengths of CADe and WE complement the weaknesses of each other in optimizing polyp detection.

Computer-Aided Colon Polyp Detection on High Resolution Colonoscopy Using Transfer Learning Techniques

Colonoscopies reduce the incidence of colorectal cancer through early recognition and resecting of the colon polyps. However, the colon polyp miss detection rate is as high as 26% in conventional colonoscopy. The search for methods to decrease the polyp miss rate is nowadays a paramount task. A number of algorithms or systems have been developed to enhance polyp detection, but few are suitable for real-time detection or classification due to their limited computational ability. Recent studies indicate that the automated colon polyp detection system is developing at an astonishing speed. Real-time detection with classification is still a yet to be explored field. Newer image pattern recognition algorithms with convolutional neuro-network (CNN) transfer learning has shed light on this topic. We proposed a study using real-time colonoscopies with the CNN transfer learning approach. Several multi-class classifiers were trained and mAP ranged from 38% to 49%. Based on an Inception v2 model, a detector adopting a Faster R-CNN was trained. The mAP of the detector was 77%, which was an improvement of 35% compared to the same type of multi-class classifier. Therefore, our results indicated that the polyp detection model could attain a high accuracy, but the polyp type classification still leaves room for improvement.

Current status of colon capsule endoscopy

While both the annual incidence and mortality of colorectal cancer are slowly but steadily decreasing in the United States, the incidence of such malignancy is increasing in Japan. Thus, controlling colorectal cancer in Japan is a major concern. In 2006, colon capsule endoscopy was first introduced by Eliakim et al. First-generation colon capsule endoscopy had a moderate sensitivity for detecting polyps of more than 6 mm. Thus, second-generation colon capsule endoscopy was developed to achieve higher sensitivity. Colonoscopy is the gold standard tool for colorectal cancer surveillance. With an improvement in the imaging function, the performance of second-generation colon capsule endoscopy is almost as satisfactory as that of colonoscopy. Certain situations, such as incomplete colonoscopy and contraindication for use of sedation, can benefit from colon capsule endoscopy. Colon capsule endoscopy requires a more extensive bowel preparation than colonoscopy and computed tomography colonography because it requires laxatives not only for bowel cleansing but also for promoting the excretion of the capsule. Another problem with colon capsule endoscopy includes the burden of reading and interpretation and overlook of the lesions. Currently, the development of automatic diagnosis of colon capsule endoscopy using artificial intelligence is still under progress. Although the available guidelines do not support the use of colon capsule endoscopy for inflammatory bowel disease, the possible application of colon capsule endoscopy is ulcerative colitis. This review article summarizes and focuses on the current status of colon capsule endoscopy for colorectal cancer screening and the possibility for its applicability on inflammatory bowel disease.

Real-time computer aided colonoscopy versus standard colonoscopy for improving adenoma detection rate: A meta-analysis of randomized-controlled trials

BACKGROUND

Recent prospective randomized controlled trials have evaluated deep convolutional neural network (CNN) based computer aided detection (CADe) of lesions in real-time colonoscopy. We conducted this meta-analysis to compare the adenoma detection rate (ADR) of deep CNN based CADe assisted colonoscopy to standard colonoscopy (SC) from randomized controlled trials (RCTs).

METHODS

Multiple databases were searched (from inception to May 2020) and parallel RCTs that compared deep CNN based CADe assisted colonoscopy to SC were included for this analysis. Using Mantel-Haenzel (M-H) random effects model, pooled risk ratios (RR) and mean difference (MD) were calculated. In between study heterogeneity was assessed by I2% values. Outcomes assessed included other per patient adenoma parameters.

FINDINGS

Six RCTs were included in our final analysis that utilized deep CNN based CADe system in real-time colonoscopy. Total numbers of patients assessed were 4962 (2480 in CADe and 2482 in SC group). CADe based colonoscopy demonstrated statistically higher pooled ADR, RR=1.5 (95% CI 1.3-1.72), p<0.0001, I2=56%; and pooled PDR, RR=1.42 (95% CI 1.33-1.51), p<0.00001, I2=9%; when compared to SC. Per patient adenoma detection parameters were significantly better with CADe colonoscopy when compared to SC, with increased scope withdrawal time (mean difference = 0.38, 95% CI 0.05-0.72, p = 0.02).

INTERPRETATION

Based on our meta-analysis, deep CNN based CADe colonoscopy achieved significantly higher ADR metrics, albeit with increased scope withdrawal time when compared to SC.

GIE Editorial Board top 10 topics: advances in GI endoscopy in 2019

The American Society for Gastrointestinal Endoscopy's GIE Editorial Board reviewed original endoscopy-related articles published during 2019 in Gastrointestinal Endoscopy and 10 other leading medical and gastroenterology journals. Votes from each individual member were tallied to identify a consensus list of 10 topic areas of major advances in GI endoscopy. Individual board members summarized important findings published in these 10 areas of disinfection, artificial intelligence, bariatric endoscopy, adenoma detection, polypectomy, novel imaging, Barrett's esophagus, third space endoscopy, interventional EUS, and training. This document summarizes these "top 10" endoscopic advances of 2019.