Image-enhanced endoscopy in practice

A Systematic Review and Meta-analysis of Convolutional Neural Network in the Diagnosis of Colorectal Polyps and Cancer

Convolutional neural networks are a class of deep neural networks used for different clinical purposes, including improving the detection rate of colorectal lesions. This systematic review and meta-analysis aimed to assess the performance of convolutional neural network-based models in the detection or classification of colorectal polyps and colorectal cancer. A systematic search was performed in MEDLINE, SCOPUS, Web of Science, and other related databases. The performance measures of the convolutional neural network models in the detection of colorectal polyps and colorectal cancer were calculated in the 2 scenarios of the best and worst accuracy. Stata and R software were used for conducting the meta-analysis. From 3368 searched records, 24 primary studies were included. The sensitivity and specificity of convolutional neural network models in predicting colorectal polyps in worst and best scenarios ranged from 84.7% to 91.6% and from 86.0% to 93.8%, respectively. These values in predicting colorectal cancer varied between 93.2% and 94.1% and between 94.6% and 97.7%. The positive and negative likelihood ratios varied between 6.2 and 14.5 and 0.09 and 0.17 in these scenarios, respectively, in predicting colorectal polyps, and 17.1-41.2 and 0.07-0.06 in predicting colorectal polyps. The diagnostic odds ratio and accuracy measures of convolutional neural network models in predicting colorectal polyps in worst and best scenarios ranged between 36% and 162% and between 80.5% and 88.6%, respectively. These values in predicting colorectal cancer in the worst and the best scenarios varied between 239.63% and 677.47% and between 88.2% and 96.4%. The area under the receiver operating characteristic varied between 0.92 and 0.97 in the worst and the best scenarios in colorectal polyps, respectively, and between 0.98 and 0.99 in colorectal polyps prediction. Convolutional neural network-based models showed an acceptable accuracy in detecting colorectal polyps and colorectal cancer.

Recent advances in devices and technologies that might prove revolutionary for colonoscopy procedures

INTRODUCTION

Colorectal cancer (CRC) is the third most common malignancy and second leading cause of cancer-related mortality in the world. Adenoma detection rate (ADR), a quality indicator for colonoscopy, has gained prominence as it is inversely related to CRC incidence and mortality. As such, recent efforts have focused on developing novel colonoscopy devices and technologies to improve ADR.

AREAS COVERED

The main objective of this paper is to provide an overview of advancements in the fields of colonoscopy mechanical attachments, artificial intelligence-assisted colonoscopy, and colonoscopy optical enhancements with respect to ADR. We accomplished this by performing a comprehensive search of multiple electronic databases from inception to September 2023. This review is intended to be an introduction to colonoscopy devices and technologies.

EXPERT OPINION

Numerous mechanical attachments and optical enhancements have been developed that have the potential to improve ADR and AI has gone from being an inaccessible concept to a feasible means for improving ADR. While these advances are exciting and portend a change in what will be considered standard colonoscopy, they continue to require refinement. Future studies should focus on combining modalities to further improve ADR and exploring the use of these technologies in other facets of colonoscopy.

Update on the role of chromoendoscopy in colonoscopic surveillance of patients with Lynch syndrome

(Virtual) chromoendoscopy (CE) improves the detection of small or flat colorectal polyps; however, the evidence in high-risk groups, such as patients of Lynch syndrome (LS), is low. Our aim was to identify and update the evidence for the recommendations regarding surveillance of LS patients, for which the current underlying evidence for use of (virtual) CE was explored. A systematic literature search in PubMed, EMBASE, and Cochrane library was conducted, for all studies comparing (virtual) CE with white-light endoscopy in LS patients. Studies are explained in detail, with special attention to study design, type of (virtual) CE, and timing of polypectomy. Eight studies (409 patients) were included. Five were nonrandomized back-to-back studies and three were randomized back-to-back studies (one parallel and two cross-over design). In six studies the polyps were directly removed, while in two studies polyps were removed only during the second caecal withdrawal. Five studies researched CE with indigo carmine and three studies investigated virtual CE. Due to the heterogeneity between studies, no statistical analysis could be performed. There was a large variety in study design, timing of polypectomy, different (virtual) CE techniques and the patients that were included. Based on current literature, no firm conclusions can be drawn with respect to the additional value of (virtual) CE in the surveillance of patients with LS. However, training of endoscopists in detection and removal of nonpolypoid colorectal neoplasms is crucial, as well as stricter adherence to LS surveillance guidelines in daily clinical practice. For future research, standardization in study designs is needed.