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Since linked color imaging (LCI) has been reported to increase the color differences in Barrett’s esophageal adenocarcinoma (BA) compared to white light imaging (WLI), a comparison of the visibility scores of various imaging techniques for BA is warranted to determine best practice standards. This study is to clarify the role of LCI, blue light imaging (BLI), and WLI in the evaluation of BA. A group of 19 endoscopists, comprised of 6 experts and 13 trainees, evaluated the visibility of WLI, BLI, and LCI images in 21 superficial BA cases. Visibility scores were compared between WLI, BLI, and LCI. Visibility scores were also evaluated for lesion morphology, background Barrett’s mucosa, and circumferential location. The visibility scores of experts and trainees were analyzed for comparison. The visibility scores of LCI and BLI were 3.83 and 3.31, respectively, compared to three points for WLI. The visibility of LCI was better than that of WLI regardless of lesion morphology, color, background Barrett’s mucosa, and circumferential location. The LCI improved visibility in BA more than the WLI for both experts and trainees. LCI improved the visibility of BA independent of lesion morphology, color, background Barrett’s mucosa, circumferential location, and the endoscopist’s experience.

Abstract Objectives Although the laser light is optically ideal for producing narrow‐band light, it has not been used in some areas of the world. Endoscopic light sources using light‐emitting diodes (LEDs) are used worldwide. The purpose of this study was to compare blue laser imaging (laser‐BLI) and LED‐blue light imaging (LED‐BLI) for the characterization of colorectal polyps using the Japan narrow band imaging expert team (JNET) classification. Methods Colorectal lesions were prospectively examined using magnifying narrow‐band light generated by a laser (laser‐BLI) or LEDs (LED‐BLI). Twelve endoscopists (six non‐experts and six experts from three institutions) evaluated each still‐magnified image of lesions using the JNET classification. Results Seven hundred and fifty‐six images from 63 lesions were reviewed. The mean polyp size was 24.5 ± 13.4 mm. Histopathology included 13 serrated lesions and 50 neoplasms. The rate of agreement between laser‐BLI and LED‐BLI using the JNET classification was 92.5% (699/756). The weighted κ‐statistic was 0.99. The percentages of “almost similar” comparing scores of surface patterns, vessel patterns, and brightness among all endoscopists were 95.4%, 95.9%, and 95.0%, respectively. Conclusions This multicenter study demonstrates that the rate of agreement between laser‐BLI and LED‐BLI using the JNET Classification is very high. The surface patterns, vessel patterns, and brightness are almost similar.

A laser endoscopy system was developed in 2012. The system allows blue laser imaging (BLI), BLI-bright, and linked color imaging (LCI) to be performed as modes of narrow-band light observation; these modes have been reported to be useful for tumor detection and characterization. Furthermore, an innovative endoscopy system using four-light emitting diode (LED) multilight technology was released in 2016 to 2017 in some areas in which laser endoscopes have not been approved for use, including the United States and Europe. This system enables blue light imaging (this is also known as BLI) and LCI with an LED light source instead of a laser light source. Several reports have shown that these modes have improved tumor detection. In this paper, we review the efficacy of BLI and LCI with laser and LED endoscopes in tumor detection and characterization.

Blue light imaging (BLI) and narrow-band imaging (NBI) are two modalities that enable narrow-band light observation. We aimed to compare the diagnostic ability of magnifying endoscopy with BLI (ME-BLI) and NBI (ME-NBI) for determining the invasion depth of superficial esophageal squamous cell carcinoma (SESCC) by the Japanese Esophageal Society’s intrapapillary capillary loop (IPCL) classification. We enrolled 81 patients between 2014 and 2018, and the still endoscopic images for diagnosing the invasion depth at the same part in ME-BLI and ME-NBI were registered. Two blinded investigators reviewed them and diagnosed the invasion depth by the IPCL classification. Subsequently, the diagnostic yields in two modalities were compared. The overall accuracies for the invasion depth by the IPCL classification in ME-BLI and ME-NBI did not differ significantly (67.9–71.6% vs. 72.8–74.1%). In the analysis based on the invasion depth, the sensitivities and positive predictive values in tumors invading the muscularis mucosa or submucosa ≤200 µm were low (23.1–30.8% and 16.7–25.0%, respectively) in both modalities. In conclusion, the diagnostic ability for determining the invasion depth of SESCC by the IPCL classification was relatively similar in ME-BLI and ME-NBI, but diagnosis by magnifying endoscopy alone might not be satisfactory.

Colorectal polyps are critical indicators of colorectal cancer (CRC). Blue Laser Imaging and Linked Color Imaging are two modalities that allow improved visualization of the colon. In conjunction with the Blue Laser Imaging (BLI) Adenoma Serrated International Classification (BASIC) classification, endoscopists are capable of distinguishing benign and pre-malignant polyps. Despite these advancements, this classification still prevails a high misclassification rate for pre-malignant colorectal polyps. This work proposes a computer aided diagnosis (CADx) system that exploits the additional information contained in two novel imaging modalities, enabling more informative decision-making during colonoscopy. We train and benchmark six commonly used CNN architectures and compare the results with 19 endoscopists that employed the standard clinical classification model (BASIC). The proposed CADx system for classifying colorectal polyps achieves an area under the curve (AUC) of 0.97. Furthermore, we incorporate visual explanatory information together with a probability score, jointly computed from White Light, Blue Laser Imaging, and Linked Color Imaging. Our CADx system for automatic polyp malignancy classification facilitates future advances towards patient safety and may reduce time-consuming and costly histology assessment.