Detection of colorectal lesions during colonoscopy.
Hiroaki IkematsuTatsuro MuranoKensuke ShinmuraPublished in: DEN open (2021)
Owing to its high mortality rate, the prevention of colorectal cancer is of particular importance. The resection of colorectal polyps is reported to drastically reduce colorectal cancer mortality, and examination by endoscopists who had a high adenoma detection rate was found to lower the risk of colorectal cancer, highlighting the importance of identifying lesions. Various devices, imaging techniques, and diagnostic tools aimed at reducing the rate of missed lesions have therefore been developed to improve detection. The distal attachments and devices for improving the endoscopic view angle are intended to help avoid missing blind spots such as folds and flexures in the colon, whereas the imaging techniques represented by image-enhanced endoscopy contribute to improving lesion visibility. Recent advances in artificial intelligence-supported detection systems are expected to supplement an endoscopist's eye through the instant diagnosis of the lesions displayed on the monitor. In this review, we provide an outline of each tool and assess its impact on the reduction in the incidence of missed colorectal polyps by summarizing previous clinical research and meta-analyses. Although useful, the many devices, image-enhanced endoscopy, and artificial intelligence tools exhibited various limitations. Integrating these tools can improve their shortcomings. Combining artificial intelligence-based diagnoses with wide-angle image-enhanced endoscopy may be particularly useful. Thus, we hope that such tools will be available in the near future.
Keyphrases
- artificial intelligence
- deep learning
- machine learning
- big data
- high resolution
- loop mediated isothermal amplification
- label free
- real time pcr
- systematic review
- meta analyses
- risk factors
- cardiovascular events
- randomized controlled trial
- type diabetes
- cardiovascular disease
- photodynamic therapy
- coronary artery disease
- minimally invasive
- quantum dots