Background: In their early phase, infantile hemangiomas (IH) can sometimes be difficult to differentiate from port-wine birthmarks (PWB). Until recently, inexpensive diagnostic tools have not been readily available.
Objective: To determine the diagnostic utility of widely available colorimetric technology when differentiating PWB from IH in photographs of infants less than 3 months old.
Methods: Multi-center, retrospective analysis of RGB (red, green, and blue) and HSL (hue, saturation, lightness) values collected using electronic colorimeters from images of clinically confirmed untreated IH or PWB. Subgroup analysis of flat vascular birthmarks was subsequently performed.
Results: Images of 119 IH (specifically, 45 flat IH) and 59 PWB were identified. PWB had significantly (P < .001) higher RGB values of all primary colors, most notably for blue and green (mean difference: >50), irrespective of thickness. RGB or RGB with HSL values had an excellent accuracy (90%), sensitivity (92%), specificity (98%), and positive predictive value (98%) when discriminating PWB from flat IH. IH could be distinctly clustered from PWB when combining their RGB with HSL values.
Conclusion: Electronic colorimeters with emphasis on blue and green values, are able to differentiate PWB from IH, irrespective of thickness, with a high degree of accuracy. A larger scale evaluation is now required.
Keywords: colorimetry; diagnosis; infantile hemangioma; machine learning; port-wine birthmark.
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