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Accepted for/Published in: JMIR Dermatology

Date Submitted: Feb 6, 2020
Date Accepted: Feb 27, 2020

The final, peer-reviewed published version of this preprint can be found here:

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

Eapen BR, Archer N, Sartipi K

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

JMIR Dermatol 2020;3(1):e18149

DOI: 10.2196/18149

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

LesionMap: A method and a tool for the semantic annotation of dermatological lesions for documentation and machine learning

  • Bell Raj Eapen; 
  • Norm Archer; 
  • Kamran Sartipi

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 Lesion- Mapper (LMR) — a method and a tool 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 (EMRs). We believe that LMR will facilitate documentation in dermatology that can be used for machine learning in a privacy-preserving manner.


 Citation

Please cite as:

Eapen BR, Archer N, Sartipi K

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

JMIR Dermatol 2020;3(1):e18149

DOI: 10.2196/18149

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