Combines CRFF-OCT and Artificial Intelligence to Improve Objective Assessment of Vitiligo Disease Activity

🧬 If vitiligo lesions appear unchanged, does that necessarily mean the disease is stable?

This is one of the most common questions we hear in the clinic—and one of the most challenging to answer objectively.

We are delighted to share that our latest research has been published in Experimental Dermatology.

In this study, we used Cellular-resolution Full-field Optical Coherence Tomography (CRFF-OCT) to investigate the microstructural features of vitiligo skin. We found that active and stable vitiligo exhibit distinct characteristics at the dermal–epidermal junction (DEJ), in pigment reflectance, and in epidermal architecture. By integrating these imaging features with artificial intelligence (AI), we developed a machine learning model capable of identifying active disease with approximately 80% accuracy.

We hope that this technology will provide a more objective approach to assessing disease activity, helping clinicians determine the optimal timing for treatment, monitor therapeutic responses, and ultimately advance precision medicine for patients with vitiligo.

We would like to express our sincere gratitude to all members of our research team, our collaborators, and especially every patient who participated in this study. Your trust and support have enabled us to transform important clinical questions into research that has the potential to improve patient care.

🔗 Publication:https://onlinelibrary.wiley.com/doi/10.1111/exd.70309

Experimental Dermatology (2026)

Quantitative CRFF-OCT Imaging Features for Characterization of Disease Activity in Non-Segmental Vitiligo: A Machine Learning–Assisted Study