LesionMap: A Method and Tool for the Semantic Annotation of Dermatological Lesions for Documentation and Machine Learning

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ID: 112428
2020
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Abstract
Diagnosis and follow-up of patients in dermatology rely on visual cues. Documentation of skin lesions in dermatology is time-consuming and inaccurate. Digital photography is resource-intensive, difficult to standardize, and has privacy concerns. We propose a simple method—LesionMap—and an electronic health software tool—LesionMapper—for semantically annotating dermatological lesions on a body wireframe. We discuss how the type, distribution, and progression of lesions can be represented in a standardized way. The tool is an open-source JavaScript package that can be integrated into web-based electronic medical records. We believe that LesionMapper will facilitate documentation in dermatology that can be used for machine learning in a privacy-preserving manner. [JMIR Dermatol 2020;3(1):e18149]
Reference Key
sartipi2020jmirlesionmap: Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Kamran Sartipi;
Journal jmir dermatology
Year 2020
DOI
doi:10.2196/18149
URL
https://derma.jmir.org/2020/1/e18149
Keywords
medical open access publishing Internet research medicine journal ehealth JMIR medical researchlesionmap;lesionmapper;digital imaging;machine learning;dermatology

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