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

Effects of a training system that tracks the operator's gaze pattern during endoscopic submucosal dissection on hemostasis

BACKGROUND

The early acquisition of skills required to perform hemostasis during endoscopy may be hindered by the lack of tools that allow assessments of the operator's viewpoint. Understanding the operator's viewpoint may facilitate the skills.

AIM

To evaluate the effects of a training system using operator gaze patterns during gastric endoscopic submucosal dissection (ESD) on hemostasis.

METHODS

An eye-tracking system was developed to record the operator's viewpoints during gastric ESD, displaying the viewpoint as a circle. In phase 1, videos of three trainees' viewpoints were recorded. After reviewing these, trainees were recorded again in phase 2. The videos from both phases were retrospectively reviewed, and short clips were created to evaluate the hemostasis skills. Outcome measures included the time to recognize the bleeding point, the time to complete hemostasis, and the number of coagulation attempts.

RESULTS

Eight cases treated with ESD were reviewed, and 10 video clips of hemostasis were created. The time required to recognize the bleeding point during phase 2 was significantly shorter than that during phase 1 (8.3 ± 4.1 seconds vs 23.1 ± 19.2 seconds; P = 0.049). The time required to complete hemostasis during phase 1 and that during phase 2 were not significantly different (15.4 ± 6.8 seconds vs 31.9 ± 21.7 seconds; P = 0.056). Significantly fewer coagulation attempts were performed during phase 2 (1.8 ± 0.7 vs 3.2 ± 1.0; P = 0.004).

CONCLUSION

Short-term training did not reduce hemostasis completion time but significantly improved bleeding point recognition and reduced coagulation attempts. Learning from the operator's viewpoint can facilitate acquiring hemostasis skills during ESD.

Eye-tracking dataset of endoscopist-AI teaming during colonoscopy: Retrospective and real-time acquisition

Recent studies have demonstrated that integrating AI into colonoscopy procedures significantly improves the adenoma detection rate (ADR) and reduces the adenoma miss rate (AMR). However, few studies address the critical issue of endoscopist-AI collaboration in real-world settings. Eye-tracking data collection is considered a promising approach to uncovering how endoscopists and AI interact and influence each other during colonoscopy procedures. A common limitation of existing studies is their reliance on retrospective video clips, which fail to capture the dynamic demands of real-time colonoscopy, where endoscopists must simultaneously navigate the colonoscope and identify lesions on the screen. To address this gap, we established a dataset to analyze changes in endoscopists' eye movements during the colonoscopy withdrawal phase. Eye-tracking data was collected from graduate students, nurses, senior endoscopists, and novice endoscopists while they reviewed retrospectively recorded colonoscopy withdrawal videos, both with and without computer-aided detection (CADe) assistance. Furthermore, 80 real-time video segments were prospectively collected during endoscopists' actual colonoscopy withdrawal procedures, comprising 43 segments with CADe assistance and 37 segments without assistance (normal control).

Wearable Near-Eye Tracking Technologies for Health: A Review

With the rapid advancement of computer vision, machine learning, and consumer electronics, eye tracking has emerged as a topic of increasing interest in recent years. It plays a key role across diverse domains including human-computer interaction, virtual reality, and clinical and healthcare applications. Near-eye tracking (NET) has recently been developed to possess encouraging features such as wearability, affordability, and interactivity. These features have drawn considerable attention in the health domain, as NET provides accessible solutions for long-term and continuous health monitoring and a comfortable and interactive user interface. Herein, this work offers an inaugural concise review of NET for health, encompassing approximately 70 related articles published over the past two decades and supplemented by an in-depth examination of 30 literatures from the preceding five years. This paper provides a concise analysis of health-related NET technologies from aspects of technical specifications, data processing workflows, and the practical advantages and limitations. In addition, the specific applications of NET are introduced and compared, revealing that NET is fairly influencing our lives and providing significant convenience in daily routines. Lastly, we summarize the current outcomes of NET and highlight the limitations.

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).