Developing a Novel Pipeline for Visualising Anatomic Epidemiological Data

Abstract Background: Although custom anatomic heatmaps can serve as excellent analytical aids in epidemiological studies, there is no general, widely utilised approach for producing them. Aim: Presenting a pipeline for rapidly generating two-dimensional heatmaps for anatomic data. Setting: Population health studies that conduct anatomy-based comparisons. Methods: Two freehand full-body templates were drawn, encoded, and programmatically painted. Results: Six high-resolution, data-tailored heatmaps, summarising 7298 cancer cases, were rapidly generated. These visualisations clearly portrayed the similarities and differences between study populations, and could be updated to reflect new data almost immediately. Conclusion: This study provides a flexible algorithm that investigators can use to generate and share heatmaps for anatomic health data. By cleanly contextualising data within its generative framework, this approach can contribute to higher-quality and more impactful scientific reports. Contribution: This methodology provides a concise, replicable way to visualise qualitative differences in the distribution of various large-scale biomedical phenomena within human anatomy. Keywords: visualisation, heatmap, scalable vector graphics, algorithm, body map