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Background: Perceptual and adaptive learning modules (PALM's) provide a large number of visual examples for evaluation and accommodate to learner performance by actively adjusting the module parameters. Methods: We developed a module for discriminating 5 inflammatory reaction patterns using the Novel Diagnostic Educational Resource (NDER) platform. The module included a 20 question pre-test, a 200 question training section, and a 20 question post-test. During the pre-test and post-test, images were displayed for an indefinite period of time with no feedback given. In the training section, images were displayed for a duration inverse to learner performance, and after submitting their response learners were immediately shown the correct answer. The performance of module participants was compared to a control group who completed pre-test and post-test only. Results: 26 pathology and dermatology residents completed the module and were included in analysis. Pre-test and post-test scores showed an average increase of 17.1 percentage points (95% CI 13.0 to 21.2, P < 0.001). When performance on pre-test and post-test was compared between the module and control groups, module group performance increased more than control group performance by an average of 10.1 percentage points (95% CI -2.5 to 17.8, P = 0.0119). 84% (37) of participants found the module somewhat useful or very useful and 68% (30) of participants would be pretty likely or very likely to recommend to another trainee. Conclusions: Our findings validate the use of NDER for teaching inflammatory reaction patterns. Participants generally had favorable feedback regarding the interface and teaching potential of the module. Including a late re-test as part of the module would be beneficial in further validating future iterations. Next steps include optimizing module performance and developing module content for more advanced learners.
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Medical students require a strong foundation in normal histology. However, current trends in medical school curricula have diminished time devoted to histology. Thus, there is a need for more efficient methods of teaching histology. We have developed a novel software program (Novel Diagnostic Educational Resource; https://pcs-webtest0.pathology.washington.edu/academics/pattern/) that uses annotated whole slide images to teach normal histology. Whole slide images of a wide variety of tissues were annotated by a trainee and validated by an experienced pathologist. Still images were extracted and transferred to the Novel Diagnostic Educational Resource web application. In Novel Diagnostic Educational Resource, an image was displayed briefly and the user was forced to identify the tissue type. The display time changed inversely based on cumulative accuracy to challenge the user and maintain engagement. A total of 129 second-year medical students completed the 30-minute Novel Diagnostic Educational Resource module. Surveys showed an increase in confidence from premodule (0% extremely confident, 4% very, 47% somewhat, and 49% not) to postmodule (9% extremely confident, 57% very, 32% somewhat, and 2% not), P < .0001. Accuracy increased from 72.6% pretest to 95.7% posttest, P < .002. The effect size (Cohen d = 2.30) was very large, where 0.2 is a small effect, 0.5 moderate, and 0.8 large. Ninety-six percent of students would recommend Novel Diagnostic Educational Resource to other medical students, and 98% would use Novel Diagnostic Educational Resource to further enhance their histology knowledge. Novel Diagnostic Educational Resource drastically improved medical student accuracy in classifying normal histology and improved confidence. Additional study is needed to determine knowledge retention, but Novel Diagnostic Educational Resource has great potential for efficient teaching of histology given the curriculum time constraints in medical education.
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CONTEXT: Whole-slide images (WSIs) present a rich source of information for education, training, and quality assurance. However, they are often used in a fashion similar to glass slides rather than in novel ways that leverage the advantages of WSI. We have created a pipeline to transform annotated WSI into pattern recognition training, and quality assurance web application called novel diagnostic electronic resource (NDER). AIMS: Create an efficient workflow for extracting annotated WSI for use by NDER, an attractive web application that provides high-throughput training. MATERIALS AND METHODS: WSI were annotated by a resident and classified into five categories. Two methods of extracting images and creating image databases were compared. Extraction Method 1: Manual extraction of still images and validation of each image by four breast pathologists. Extraction Method 2: Validation of annotated regions on the WSI by a single experienced breast pathologist and automated extraction of still images tagged by diagnosis. The extracted still images were used by NDER. NDER briefly displays an image, requires users to classify the image after time has expired, then gives users immediate feedback. RESULTS: The NDER workflow is efficient: annotation of a WSI requires 5 min and validation by an expert pathologist requires An additional one to 2 min. The pipeline is highly automated, with only annotation and validation requiring human input. NDER effectively displays hundreds of high-quality, high-resolution images and provides immediate feedback to users during a 30 min session. CONCLUSIONS: NDER efficiently uses annotated WSI to rapidly increase pattern recognition and evaluate for diagnostic proficiency.
