Abstract
Intralesional triamcinolone acetonide (ILTA) is a widely used first-line corticosteroid therapy for keloid disorder, yet many patients show limited or no response, while a significant number see a worsening of their keloids. Given the variability in steroid treatment outcomes, there is a pressing need for tools that can predict patients' steroid responsiveness prior to initiating therapy. We hypothesized that machine learning (ML) models trained on keloid patient-reported data could be leveraged to provide accurate, pre-treatment predictions for their response to steroid therapy, enabling more personalized and effective treatments for keloid patients.