Training visual pattern recognition in ophthalmology using a perceptual and adaptive learning module.

OBJECTIVE: To evaluate the efficacy of a perceptual and adaptive learning module (PALM) for teaching the identification of 5 optic nerve findings. METHODS: Second- through fourth-year medical students were randomized to the PALM or a video didactic lecture. The PALM presented the learner with short classification tasks consisting of optic nerve images. Learner accuracy and response time guided the sequencing of successive tasks until mastery was achieved. The lecture was a narrated video designed to simulate a portion of a traditional medical school lecture. Accuracy and fluency on a pretest, post-test, and 1-month delayed test were compared within and between groups. RESULTS: Eighty-three students participated. Accuracy and fluency improved significantly (p < 0.001) from pretest to post-test for both the PALM (accuracy, Cohen's d = 2.94; fluency, d = 3.39) and the lecture (accuracy, d = 2.32; fluency, d = 1.06). For the delayed test, PALM performance remained significantly greater (p < 0.001) than the pretest in both accuracy (d = 0.89) and fluency (d = 1.16), whereas lecture performance remained greater in accuracy only (d = 0.44; p = 0.02). CONCLUSIONS: The PALM facilitated visual pattern recognition for optic nerve diseases among novice learners using a single brief self-guided session. The PALM may be applied alongside traditional didactic lectures to expedite visual pattern recognition in ophthalmology.

Connecting Adaptive Perceptual Learning and Signal Detection Theory in Skin Cancer Screening.

Combining perceptual learning techniques with adaptive learning algorithms has been shown to accelerate the development of expertise in medical and STEM learning domains (Kellman & Massey, 2013; Kellman, Jacoby, Massey & Krasne, 2022). Virtually all adaptive learning systems have relied on simple accuracy data that does not take into account response bias, a problem that may be especially consequential in multi-category perceptual classifications. We investigated whether adaptive perceptual learning in skin cancer screening can be enhanced by incorporating signal detection theory (SDT) methods that separate sensitivity from criterion. SDT-style concepts were used to alter sequencing, and separately to define mastery (category retirement). SDT retirement used a running d' estimate calculated from a recent window of trials based on hit and false alarm rates. Undergraduate participants used a Skin Cancer PALM (perceptual adaptive learning module) to learn classification of 10 cancerous and readily-confused non-cancerous skin lesion types. Four adaptive conditions varied either the type of adaptive sequencing (standard vs. SDT) or retirement criteria (standard vs. SDT). A non-adaptive control condition presented didactic instruction on dermatologic screening in video form, including images, classification schemes, and detailed explanations. All adaptive conditions robustly outperformed the non-adaptive control in both learning efficiency and fluency (large effect sizes). Between adaptive conditions, SDT retirement criteria produced greater learning efficiency than standard, accuracy-based mastery criteria at both immediate and delayed posttests (medium effect sizes). SDT sequencing and standard adaptive sequencing did not differ. SDT enhancements to adaptive perceptual learning procedures have potential to enhance learning efficiency.

Novel Education Modules Addressing the Underrepresentation of Skin of Color in Dermatology Training.

BACKGROUND: Representative images of pathology in patients with skin of color are lacking in most medical education resources. This particularly affects training in dermatology, which relies heavily on the use of images to teach pattern recognition. The presentation of skin pathology can vary greatly among different skin tones, and this lack of representation of dark skin phototypes challenges providers' abilities to provide quality care to patients of color.In Botswana and other countries in sub-Saharan Africa, this challenge is further compounded by limited resources and access to dermatologists. There is a need for improved and accessible educational resources to train medical students and local medical providers in basic skin lesion description and diagnosis. OBJECTIVES: We examined whether online Perceptual and Adaptive Learning Modules (PALMs) composed of representative dark skin images could efficiently train University of Botswana medical students to more accurately describe and diagnose common skin conditions in their community. METHODS: Year 4 and 5 medical students voluntarily completed PALMs that teach skin morphology, configuration, and distribution terminology and diagnosis of the most common dermatologic conditions in their community. Pre-tests, post-tests and delayed-tests assessed knowledge acquisition and retention. RESULTS: PALMs training produced statistically significant (P < .0001) improvements in accuracy and fluency with large effect sizes (1.5, 3.7) and good retention after a 12.5-21-week median delay. Limitations were a self-selected group of students, a single institution, slow internet connections, and high drop-out rates. CONCLUSIONS: Overall, population-specific PALMs are a useful tool for efficient development of pattern recognition in skin disease description and diagnosis.

Applying perceptual and adaptive learning techniques for teaching introductory histopathology.

BACKGROUND: Medical students are expected to master the ability to interpret histopathologic images, a difficult and time-consuming process. A major problem is the issue of transferring information learned from one example of a particular pathology to a new example. Recent advances in cognitive science have identified new approaches to address this problem. METHODS: We adapted a new approach for enhancing pattern recognition of basic pathologic processes in skin histopathology images that utilizes perceptual learning techniques, allowing learners to see relevant structure in novel cases along with adaptive learning algorithms that space and sequence different categories (e.g. diagnoses) that appear during a learning session based on each learner's accuracy and response time (RT). We developed a perceptual and adaptive learning module (PALM) that utilized 261 unique images of cell injury, inflammation, neoplasia, or normal histology at low and high magnification. Accuracy and RT were tracked and integrated into a "Score" that reflected students rapid recognition of the pathologies and pre- and post-tests were given to assess the effectiveness. RESULTS: Accuracy, RT and Scores significantly improved from the pre- to post-test with Scores showing much greater improvement than accuracy alone. Delayed post-tests with previously unseen cases, given after 6-7 weeks, showed a decline in accuracy relative to the post-test for 1(st)-year students, but not significantly so for 2(nd)-year students. However, the delayed post-test scores maintained a significant and large improvement relative to those of the pre-test for both 1(st) and 2(nd) year students suggesting good retention of pattern recognition. Student evaluations were very favorable. CONCLUSION: A web-based learning module based on the principles of cognitive science showed an evidence for improved recognition of histopathology patterns by medical students.