ABSTRACT
Considering its contribution to reducing colorectal cancer morbidity and mortality, the most important task of colonoscopy is to find all existing polyps. Moreover, the accurate detection of existing polyps determines the risk of colorectal cancer morbidity and is an important factor in deciding the appropriate surveillance program for patients. Image-enhanced endoscopy is an easy-to-use modality with improved lesion detection. Linked color imaging (LCI) and blue laser/light imaging (BLI) are useful modalities for improving colonoscopy quality. Each mode has unique optical features; therefore, their intended use differs. LCI contributes to improved polyp detection due to its brightness and high color contrast between the lesion and normal mucosa, while BLI contributes to the characterization of detected polyps by evaluating the vessel and surface patterns of detected lesions. The proper use of these observation modes allows for more efficient endoscopic diagnosis. Moreover, recent developments in artificial intelligence will soon change the clinical practice of colonoscopy and this system will provide an efficient education modality for novice endoscopists.
ABSTRACT
Considering its contribution to reducing colorectal cancer morbidity and mortality, the most important task of colonoscopy is to find all existing polyps. Moreover, the accurate detection of existing polyps determines the risk of colorectal cancer morbidity and is an important factor in deciding the appropriate surveillance program for patients. Image-enhanced endoscopy is an easy-to-use modality with improved lesion detection. Linked color imaging (LCI) and blue laser/light imaging (BLI) are useful modalities for improving colonoscopy quality. Each mode has unique optical features; therefore, their intended use differs. LCI contributes to improved polyp detection due to its brightness and high color contrast between the lesion and normal mucosa, while BLI contributes to the characterization of detected polyps by evaluating the vessel and surface patterns of detected lesions. The proper use of these observation modes allows for more efficient endoscopic diagnosis. Moreover, recent developments in artificial intelligence will soon change the clinical practice of colonoscopy and this system will provide an efficient education modality for novice endoscopists.