Development of an artificial intelligence algorithm for the diagnosis of infantile hemangiomas

April J. Zhang*, Nick Lindberg, Sarah L. Chamlin, Anita N. Haggstrom, Anthony J. Mancini, Dawn H. Siegel, Beth A. Drolet

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Prompt and accurate diagnosis of infantile hemangiomas is essential to prevent potential complications. This can be difficult due to high rates of misdiagnosis and poor access to pediatric dermatologists. In this study, we trained an artificial intelligence algorithm to diagnose infantile hemangiomas based on clinical images. Our algorithm achieved a 91.7% overall accuracy in the diagnosis of facial infantile hemangiomas.

Original languageEnglish (US)
Pages (from-to)934-936
Number of pages3
JournalPediatric dermatology
Volume39
Issue number6
DOIs
StatePublished - Nov 1 2022

Keywords

  • artificial intelligence
  • hemangioma
  • infants
  • machine learning
  • neoplasms
  • vascular tissue

ASJC Scopus subject areas

  • Dermatology
  • Pediatrics, Perinatology, and Child Health

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