Burn care management includes assessing the severity of burns accurately, especially distinguishing superficial partial thickness (SPT) burns from deep partial thickness (DPT) burns, in the context of providing definitive, downstream treatment. Moreover, the healing of the wound in the sub-acute care setting requires continuous tracking to avoid complications. Artificial intelligence (AI) and computer vision (CV) provide a unique opportunity to build low-cost and accessible tools to classify burn severity and track changes of wound parameters, both in the clinic by physicians and nurses, and asynchronously in the remote setting by the patient themselves. Wound assessments can be achieved by AI-CV using the principles of Image-Guided Therapy (IGT) using high-quality 2D colour images. Wound parameters can include wound 2D spatial dimension and the characterization of wound colour changes which demonstrates physiological changes such as presentation of eschar/necrotic tissue, pustulence, granulation tissue and scabbing. Here we present the development of AI-CV-based Skin Abnormality Tracking Algorithm (SATA) pipeline.Additionally we provide proof-of-concept results on a severe localized burn tracked for a 6-week period in clinic, and an additional 2-week period of home monitoring.