Abstract
A web-based image classification tool (DiLearn) was developed to facilitate active learning in the oral health profession. Students engage with oral lesion images using swipe gestures to classify each image into pre-determined categories (e.g., left for refer and right for no intervention). To assemble the training modules and to provide feedback to students, DiLearn requires each oral lesion image to be classified, with various features displayed in the image. The collection of accurate meta-information is a crucial step for enabling the self-directed active learning approach taken in DiLearn. The purpose of this study is to evaluate the classification consistency of features in oral lesion images by experts and students for use in the learning tool. Twenty oral lesion images from DiLearn’s image bank were classified by three oral lesion experts and two senior dental hygiene students using the same rubric containing eight features. Classification agreement among and between raters were evaluated using Fleiss’ and Cohen’s Kappa. Classification agreement among the three experts ranged from identical (Fleiss’ Kappa = 1) for “clinical action”, to slight agreement for “border regularity” (Fleiss’ Kappa = 0.136), with the majority of categories having fair to moderate agreement (Fleiss’ Kappa = 0.332–0.545). Inclusion of the two student raters with the experts yielded fair to moderate overall classification agreement (Fleiss’ Kappa = 0.224–0.554), with the exception of “morphology”. The feature of clinical action could be accurately classified, while other anatomical features indirectly related to diagnosis had a lower classification consistency. The findings suggest that one oral lesion expert or two student raters can provide fairly consistent meta-information for selected categories of features implicated in the creation of image classification tasks in DiLearn.
Highlights
Exposure to oral lesion images is an important component of dental education and oral pathology education
By introducing a new tool for image classification such as DiLearn, faculties from dentistry and dental hygiene can integrate active learning into oral pathology, which is traditionally delivered in a didactic format
That the collection of accurate meta-information is a crucial step for enabling the self-directed active learning approach taken in DiLearn
Summary
Exposure to oral lesion images is an important component of dental education and oral pathology education. The process of interpreting a diagnostic image is generally divided into three phases [1], namely: (1) psycho-physical, which identifies the appropriate examination technique and human anatomical system; (2) psychological, which provides a higher cognitive process in organizing the images into precepts; and (3) nosological, which integrates the medical knowledge required to produce a diagnosis. To effectively train students to produce a diagnosis based on diagnostic images, students are required to process the image, incorporating an array of information such as the location, size, opacity, medical history, demographic information, and underlying histology [2]. While students are often given longitudinal case information, this inductive approach trains students through a step-by-step manner, drawing on their nuanced understanding of clinical information, features of the image, and a cognitive understanding to interpret the image [3]. Rather than focusing on interpreting entire cases at the outset, our study will focus on providing students with opportunities to familiarize themselves with the definitions, signs, and features associated with various abnormalities; features that are required to interpret images in the nosological phase [3]
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