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Noninvasive Quantification of Skin Pigmentation after Light Based Therapy: A Pilot Study
David M. Turer, MD, MS; Isaac B. James, MD; Barry E. DiBernardo, MD;
University of Pittsburgh Medical Center
2020-01-28
Presenter: David M Turer
Affidavit:
All work completed in this study was performed by the residents named as authors.
Director Name: Vu Nguyen
Author Category: Resident Plastic Surgery
Presentation Category: Clinical
Abstract Category: Aesthetics
Background: Light-based therapies are a mainstay in the treatment of dyspigmentation. Most noninvasive methods of quantifying pigmentation are either cumbersome or are based on skin color measurement and suffer from a lack of repeatability. Most investigators use subjective assessment as the primary method to quantify treatments of dyspigmentation. In this study, we present a new method of quantifying pigmentation using artificial intelligence assisted noninvasive imaging.
Methods: Subjects underwent treatment for dyspigmentation with BroadBand Light (BBL). Pre and post procedure imaging was performed with the Canfield IntelliStudio and Vectra XT systems. Specifically, high resolution cross-polarized images were captured using the IntelliStudio to allow for quantification of pigmentation. 3D images were obtained to measure the treated surface area. Pigmented lesions were automatically identified and their area and pigment levels were quantified and compared to post-treatment images to determine the improvement in pigmentation. Using the calculated surface area, energy delivered per unit area could then be calculated.
Results: Nine female subjects underwent treatment with BBL. Visible spot size and area were reduced by 42.8ą25.1% and 46.1ą24.0% respectively. Brown spot size and area were reduced by 10.5ą28% and 9.1ą35% respectively. Red spot size and area were reduced by 8.6ą12.3 and 2.5ą20.2% respectively. Average energy delivered per unit area for the 515nm and 560nm filters were 10.6ą2.6 J/cm2 and 10.4ą3.6 J/cm2.
Conclusion: This study demonstrates the feasibility of quantifying improvement in dyspigmentation using artificial intelligence based algorithms. This technique could be used to precisely fine-tune treatment settings, leading to improved outcomes and patient safety.
