Identification of gaze pattern and blind spots by upper gastrointestinal endoscopy using an eye-tracking technique

Applications of artificial intelligence in gastroscopy: a narrative review

Gastroscopy, a critical tool for the diagnosis of upper gastrointestinal diseases, has recently incorporated artificial intelligence (AI) technology to alleviate the challenges involved in endoscopic diagnosis of some lesions, thereby enhancing diagnostic accuracy. This narrative review covers the current status of research concerning various applications of AI technology to gastroscopy, then discusses future research directions. By providing this review, we hope to promote the integration of gastroscopy and AI technology, with long-term clinical applications that can assist patients.

Eye tracking technology in endoscopy: Looking to the future

The visual patterns of an endoscopist, that is, what the endoscopist is looking at during luminal endoscopy, is an interesting area with an evolving evidence base. The tools required for gaze analysis have become cheaper and more easily accessible. A comprehensive literature search was undertaken identifying 19 relevant papers. Gaze analysis has been used to identify certain visual patterns associated with higher polyp detection rates. There have also been increasing applications of gaze analysis as an objective study tool to compare the effectiveness of endoscopic imaging technologies. Gaze analysis also has the potential to be incorporated into endoscopic training. Eye movements have been used to control and steer a robotic endoscope. This review presents the current evidence available in this novel and evolving field of endoscopic research.

Improvement of detection sensitivity of upper gastrointestinal epithelial neoplasia in linked color imaging based on data of eye tracking

BACKGROUND AND AIM

Linked color imaging (LCI) is useful for screening in the gastrointestinal tract; however, its true clinical benefit has not been determined. The aim of this study was to determine the objective advantage of LCI for detection of upper gastrointestinal neoplasms.

METHODS

Nine endoscopists, including three novices, three trainees, and three experts, prospectively performed eye tracking. From 30 cases of esophageal or gastric neoplasm and 30 normal cases without neoplasms, a total of 120 images, including 60 pair images of white light imaging (WLI) and LCI taken at the same positions and angles, were randomly shown for 10 s. The sensitivity of tumor detection as a primary endpoint was evaluated and sensitivities by organ, size, and visual gaze pattern were also assessed. Color differences (ΔE using CIE1976 [L*a*b*]) between lesions and surrounding mucosa were measured and compared with detectability.

RESULTS

A total of 1080 experiments were completed. The sensitivities of tumor detection in WLI and LCI were 53.7% (50.1-56.8%) and 68.1% (64.8-70.8%), respectively (P = 0.002). LCI provided higher sensitivity than WLI for the novice and trainee groups (novice: 42.2% [WLI] vs 65.6% [LCI], P = 0.003; trainee: 54.4% vs 70.0%, P = 0.045). No significant correlations were found between sensitivity and visual gaze patterns. LCI significantly increased ΔE, and the diagnostic accuracy with WLI depended on ΔE.

CONCLUSIONS

In conclusion, LCI significantly improved sensitivity in the detection of epithelial neoplasia and enabled epithelial neoplasia detection that is not possible with the small color difference in WLI. (UMIN000047944).