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

doi:10.1111/pde.15149

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

Pediatrics

Research output: Contribution to journal › Article › peer-review

2 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 language	English (US)
Pages (from-to)	934-936
Number of pages	3
Journal	Pediatric dermatology
Volume	39
Issue number	6
DOIs	https://doi.org/10.1111/pde.15149
State	Published - Nov 1 2022

Keywords

artificial intelligence
hemangioma
infants
machine learning
neoplasms
vascular tissue

ASJC Scopus subject areas

Pediatrics, Perinatology, and Child Health
Dermatology

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10.1111/pde.15149

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title = "Development of an artificial intelligence algorithm for the diagnosis of infantile hemangiomas",

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.",

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AU - Lindberg, Nick

AU - Chamlin, Sarah L.

AU - Haggstrom, Anita N.

AU - Mancini, Anthony J.

AU - Siegel, Dawn H.

AU - Drolet, Beth A.

PY - 2022/11/1

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AB - 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.

KW - artificial intelligence

KW - hemangioma

KW - infants

KW - machine learning

KW - neoplasms

KW - vascular tissue

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

Abstract

Keywords

ASJC Scopus subject areas

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