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I see something you do not: Eye movement modelling examples do not improve anomaly detection in interpreting medical images.

Authors :
Eder, Thésése F.
Scheiter, Katharina
Richter, Juliane
Keutel, Constanze
Hüttig, Fabian
Source :
Journal of Computer Assisted Learning. Apr2022, Vol. 38 Issue 2, p379-391. 13p.
Publication Year :
2022

Abstract

Background: When interpreting medical images such as dental panoramic radiographs (Orthopanthomogram, OPT), errors are frequent. Previous research has shown that eye movement modelling examples (EMME) are a supportive training method for medical image interpretation to reduce errors. To date, EMME support for OPTs has not been verified. Objectives: We investigated whether a training with EMME and verbal explanations supports dental students in evaluating OPTs. Methods: Dental students were randomly assigned to an intervention (N = 42) or a control group (N = 41). The intervention group received the EMME between pre‐ and post‐test. In a laboratory study, we measured students' gaze behaviour during evaluating OPTs and the detection rate of anomalies. Results and conclusions: The training led to fewer, shorter, and later fixations on anomalies and no difference in visual coverage of the OPT. The detection rate of anomalies did not improve. We replicated the latter finding in an online study (N = 31). Students may not have been able to apply the information from the EMME to detect anomalies. The image reading processes changed to more efficient rather than deeper visual search. Major takeaways: This study evaluated a training method with EMME for anomaly detection of OPTs. EMMEs did not improve the anomaly detection of dental students and changed their visual search process toward a more efficient rather than a deeper search. Lay Description: What is already known about this topic: Dental students and dentists commit many errors when interpreting radiographs.Validated interventions to support radiograph interpretations are lacking.Eye movement modelling examples (EMME) consist of videos showing an expert's gaze behaviour while performing a visual task.EMME improve processing of visual information in other domains (e.g., multimedia learning, chest x‐rays). What this paper adds: We investigated whether EMME improved detection of anomalies and gaze behaviour for dental students.Three EMME videos of dental experts explaining their behaviour and observations were presented to students.Studying the EMME videos did not improve the detection of anomalies.The gaze behaviour changed to a more efficient visual search. Implications for practice and/or policy: EMME do not seem to be effective as a short‐term intervention to improve anomaly detection for dental students.Further research is needed to develop effective training methods that could be applied in university teaching. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
02664909
Volume :
38
Issue :
2
Database :
Academic Search Index
Journal :
Journal of Computer Assisted Learning
Publication Type :
Academic Journal
Accession number :
155518133
Full Text :
https://doi.org/10.1111/jcal.12619