Work

Usefulness of yellow enhancement technology in laparoscopic surgery: perspectives of general surgery residents

Year: 2026

Type: article

Abstract: PURPOSE: Yellow enhancement (YE) is an image processing technology available in advanced endoscopic and laparoscopic systems. By increasing the contrast of yellow tissue tones, YE is intended to improve tissue differentiation and the identification of dissection planes. However, because YE is a relatively new technology, supporting evidence remains limited. This study qualitatively explored general surgery residents' perceptions of the usefulness of YE compared with conventional white light imaging and aimed to establish a foundation for developing a qualitatively anchored scoring system for evaluating YE. METHODS: A piloted online survey was electronically distributed to general surgery residents. After providing basic demographic information, respondents viewed videos with and without YE and then assessed their perceptions of the videos, provided general feedback, and reported their overall opinions of YE. Open-ended responses were coded inductively, followed by reflexive thematic analysis. RESULTS: A total of 34 responses were obtained. The analysis identified 7 recurring themes related to the utility of YE, including superior visualization for structure identification and enhanced contrast for tissue differentiation. Three recurring themes were noted regarding the limitations of YE, including visual distortion and fatigue, as well as issues related to transition, training, and understanding of YE. CONCLUSION: Based on video demonstrations, surgical residents generally perceived YE as beneficial in laparoscopic surgery. However, variability in perceptions highlights the subjective and context-dependent nature of YE. Ultimately, YE should be conceptualized as a specialized adjunct to, rather than a replacement for, conventional white light imaging in laparoscopic surgery.

Source: Annals of Coloproctology

DOI: 10.3393/ac.2026.00514.0073

Authors: You Qian Lin, Kai Yun Tan, Frederick H. Koh