Understanding children's attention to traumatic dental injuries using eye-tracking

The Use of Eye-Tracking Technology in Dento-Maxillofacial Esthetics: A Systematic Review

The use of eye-tracking technology in dental esthetics has gained popularity over the past decade because of its ability to assess observers' visual preferences in an objective manner. The goal of this study was to provide a comprehensive review of eye-tracking studies in dentistry, which could provide a reference for the rational and effective application of eye-tracking technology by dentists in the future. A comprehensive search of articles on eye tracking, published from January 1946 to June 2023, was conducted across several databases using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The major criterion for inclusion was that the study evaluated the use of eye-tracking technology in the field of dentistry. Two independent reviewers screened the eligible studies. A total of 67 articles were identified, 41 of which met our inclusion criteria. The most common application of eye tracking was the assessment of perceptions of changes in specific dental conditions among different classes of observers. Overall differences between groups (different classes of observers, different types of conditions) among different areas or regions of interest were analyzed. This systematic review demonstrated the utility of eye-tracking technology as a quantifiable objective assessment and emerging research tool for evaluating outcomes in several domains of dentistry.

Understanding Human Cognition Through Computational Modeling

One important goal of cognitive science is to understand the mind in terms of its representational and computational capacities, where computational modeling plays an essential role in providing theoretical explanations and predictions of human behavior and mental phenomena. In my research, I have been using computational modeling, together with behavioral experiments and cognitive neuroscience methods, to investigate the information processing mechanisms underlying learning and visual cognition in terms of perceptual representation and attention strategy. In perceptual representation, I have used neural network models to understand how the split architecture in the human visual system influences visual cognition, and to examine perceptual representation development as the results of expertise. In attention strategy, I have developed the Eye Movement analysis with Hidden Markov Models method for quantifying eye movement pattern and consistency using both spatial and temporal information, which has led to novel findings across disciplines not discoverable using traditional methods. By integrating it with deep neural networks (DNN), I have developed DNN+HMM to account for eye movement strategy learning in human visual cognition. The understanding of the human mind through computational modeling also facilitates research on artificial intelligence's (AI) comparability with human cognition, which can in turn help explainable AI systems infer humans' belief on AI's operations and provide human-centered explanations to enhance human-AI interaction and mutual understanding. Together, these demonstrate the essential role of computational modeling methods in providing theoretical accounts of the human mind as well as its interaction with its environment and AI systems.

Visual interpretation of clinical images among dental students using eye-tracking technology

BACKGROUND

Tele-consultations are increasingly used for screening and diagnosis. Only a few studies have assessed dental students' visual attention to clinical images.

AIM

To (i) determine dental students' gaze behavior, visual fixations, and diagnostic competence while viewing clinical images, and (ii) explore potential opportunities to strengthen the teaching-learning approaches.

DESIGN

Tobii Pro Nano-device captured the eye-tracking data for 65 dental undergraduate students in this cross-sectional study. The predetermined areas of interest (AOI) for all five clinical photographs were uploaded onto Tobii software. All participants used a think-aloud protocol with no restrictions to view time.

RESULTS

A total of 325 clinical pictures were analyzed, and the average view time was 189.25 ± 76.90 s. Most participants started at the center of the image (three frontal photos), spent a significant share of their view time on prominent findings, did not follow a systematic pattern, and exhibited diagnostic incompetence. Also, most participants followed a "Z" viewing pattern (oscillating movement from left to right) for the remaining two pictures.

CONCLUSIONS

Subjects frequently fixated on the prominent AOI, however, failed to make the correct diagnosis. Their view patterns revealed no sequential viewing. Therefore, emphasizing knowledge about common dental abnormalities and focusing on full coverage of clinical pictures can improve dental students' diagnostic competence and view patterns.

Reporting Eye-tracking Studies In DEntistry (RESIDE) checklist

OBJECTIVES

To (i) provide a scoping review of eye-tracking studies in dentistry, and (ii) propose a "Reporting Eye-tracking Studies in DEntistry" (RESIDE) checklist to facilitate standard reporting of eye-tracking studies.

DATA

A comprehensive search of six distinct electronic databases was undertaken.

SOURCES

Pubmed, OVID, Wed of Knowledge, Scopus, Cochrane and Google Scholar were used to identify studies that employed eye-tracking technology and dentistry as a subfield STUDY SELECTION: 42 studies met the inclusion criteria. Most studies exhibited several inconsistencies or failed to report on the appropriate items in the RESIDE checklist. These essential components include ethical approval, sample size calculation, location and setting, eye-tracking device attributes, participant calibration, sequence of events, and eye-tracking metrics (quantitative, qualitative and data details).

CONCLUSIONS

Evaluation of the published eye-tracking studies in this scoping review provides empirical data, highlighting the inconsistencies and limitations. Importantly, it illustrates the applicability of the RESIDE Checklist, which provides a comprehensive list of reporting elements to assist authors and reviewers of eye-tracking studies in dentistry. Also, RESIDE provides a framework to overcome critical issues to ensure high-quality scientific publications.

CLINICAL SIGNIFICANCE

A minimum threshold should be applied before accepting eye-tracking studies for publication in the future. RESIDE checklist promotes transparent and reproducible scientific communication about eye-tracking applications to dentistry. In addition, it provides a comprehensive list of reporting elements to assist authors and reviewers in ensuring high-quality scientific publications.