A Prototype Machine Learning Pipeline for Assessing and Tracking Keloid Scars

Dysregulated wound healing, marked by excessive collagen deposition, is the hallmark to keloid scar formation. Current methods for assessing keloids in clinical settings rely heavily on subjective measures, which are prone to interrater variability. This study introduces a machine learning (ML) pipeline prototype, designed to automate the detection, measurement, and colour analysis of keloid scars. Using a convolutional neural network (CNN), the pipeline segments keloid lesions from 2D images, applies fiducial markers for accurate size measurement, and utilizes K-Means clustering for colorimetry analysis. The CNN achieved a classification accuracy of 98% on a small test dataset. Segmentation was further refined using binary masks and contour-based detection. Colorimetry analysis revealed heterogeneity in pigmentation across keloid lesions, was varied by patient skin type, and tracked changes over time. The pipeline was validated on patients over a 5-6-month period, accurately detecting changes in keloid size and colour. While the algorithm was highly effective in most cases, challenges were noted in patients with nascent keloid or those with dark skin tones where the contrast between keloid and skin was insufficient for accurate segmentation. Additionally, early-stage keloid detection showed inconsistencies in defining lesion boundaries, particularly when keloids expanded rapidly. Despite these limitations, the ML pipeline presents a promising tool for objective keloid assessment, offering a practical, accessible, and accurate alternative to current clinical practices.