Effect of Histopathological Explanations for Dermoscopic Criteria on Learning Curves in Skin Cancer Training: a Randomized Controlled Trial

Abstract

Introduction: Case-based training improves novices pattern recognition and diagnostic accuracy in skin cancer diagnostics. However, it is unclear how pattern recognition is best taught in conjunction with the knowledge needed to justify a diagnosis.

Objectives: The aim of this study was to examine whether an explanation of the underlying histopathological reason for dermoscopic criteria improves skill acquisition and retention during case-based training in skin cancer diagnostics.

Methods: In this double-blinded randomized controlled trial, medical students underwent eight days of case-based training in skin cancer diagnostics, which included access to written diagnosis modules. The modules dermoscopic subsections differed between the study groups. All participants received a general description of the criteria, but the intervention group additionally received a histopathological explanation.

Results: Most participants (78%) passed a reliable test in skin cancer diagnostics, following a mean training time of 217 minutes. Access to histopathological explanations did not affect participants' learning curves or skill retention.

Conclusions: The histopathological explanation did not affect the students, but the overall educational approach was efficient and scalable.

Figure 1

Trial flow. Participants performed a pre-test (12 cases) at the beginning and a retention test (25 cases) at the end of the trial. During the training and retention phases, each participant practiced skin lesion diagnostics on 500 (including the pre-test cases) and 100 training cases, respectively, while accessing the learning modules of their own accord. We instructed participants to abstain from any training during both washout phases.

Figure 2

Educational intervention (mobile application). The educational intervention consisted of an educational mobile application that included quizzes and written learning modules. The red circles within the figure indicate where users “press” the mobile screen to proceed towards the next screen, indicated by the red arrow. The “Quiz feature” presents skin lesions for diagnostics. The small images representing the clinical image (A) and the avatar (B) are buttons that open the clinical image and 3D avatar. When users press “Benign” (C) or “Malignant”, an array of new buttons representing the various benign or malignant differential diagnoses appear. When users press one of the diagnosis buttons (D), they receive immediate feedback. The feedback consists of the chosen diagnosis, the correct diagnosis, and access to learning modules on both the chosen and correct diagnoses (E). Each learning module consists of the following sections: introduction, pathology, clinical presentation, dermoscopy, differential diagnoses, and references. The dermoscopy sections included an overview and subsections describing the primary dermoscopic criteria. Each subsection included a detailed description of the dermoscopic criterium (F). Users from both trial groups received descriptions and annotated images representing the dermoscopic criteria. However, the subsections presented to the intervention group participants also explained the underlying histopathological correlation for the dermoscopic criteria. When the learning modules in the application are closed (G), users return to the previous training case feedback page.

Figure 3

Consort diagram.

Figure 4

Learning curve models for the training phase. The figure depicts the various statistical equations applied to the data. Red dashed, and solid black lines represent the intervention and control groups, respectively. (B) An almost straight line with one knot provided a simple yet reliable fit for the data. (C,D) Increasing the complexity of the model by introducing additional knots (C) or a cubic function (D) did not provide any additional value compared to model B.

Figure 5

Learning curves during the training and retention phases. The diagram depicts the learning curves for the entire group (intervention: dark blue, control: red), the high-performance group (intervention: light blue, control: orange), and the low-performance group (intervention: green, control: purple).