Aim:The aim of this group is to elucidate visual expertise in radiographic diagnostics.
The ability to read medical images (e.g., panoramic X-rays) is an important skill required in medicine as well as in dentistry. Previous studies showed that reading X-rays is an error-prone process, with proficient performance requiring an extensive amount of practice.
The study group established a set of projects to elucidate visual expertise in radiographic diagnostics. Namely
- The process of teaching in the example of dentistry (in-lab study)
- The expertise of practioners in dentistry and radiology (field study)
- Development of teaching applications to foster expertise in novices and experts. (in-lab study)
Despite its relevance, evidence-based methods for teaching medical image reading to novice students are lacking. This aspect will be addressed by the current project.
Project #1 was started and funded in Spring 2017 by the Leibniz ScienceCampus "Cognitive Interfaces"
In the project we aim at gathering insights on students’ information processing during (learning about) medical image reading in dental medicine and at designing novel teaching methods to support knowledge work. As regards information design, we will study the effects of different visualizations of another person’s eye movements superimposed onto medical images on learning. With respect to interaction design, an adaptive gaze-based training environment will be created, where students’ eye movements during image processing are analyzed online and individualized feedback is given. In four in-situ studies, embedded in the students’ regular courses, we will evaluate current educational practice as well as the developed training methods. In study 1 we will investigate the effects of massed practice on visual processing and diagnostic accuracy as a baseline; moreover, we will also study medial image reading in various student populations that differ in their domain knowledge and task experience. In studies 2 and 3 we address the add-on influence of targeted interventions based on eye movement modeling, whereas in study 4, we will investigate the effectiveness of gaze-based adaptive feedback.
Project #2 was voted positive by the Instititional Review Board in summer 2017 and is in set up.
This project collects data from experience dentsits and radiologists in clinical setting. Their visual paths will be analyse by novel algorithms to detect succesful patterns. These data will be compared with data from project #1 to develop teaching formats and applications for pre- and postgraduate trainings.
This development will be covered in Project #3