title: Non-invasive estimation of skin thickness from hyperspectral imaging and validation using echography.
abstract: BACKGROUND
  The skin is the largest organ and is subject to the greatest exposure to outside elements throughout one׳s lifetime. Current data by the American Academy of Dermatology suggests that approximately ten people die each hour worldwide due to skin related conditions. Cancers such as melanoma are growths that originate in the epidermis. Therefore, an accurate and non-invasive method to estimate skin constitutive elements can play an important clinical role in detecting the early onset of skin tumors. It can also serve as a valuable tool for research and development in cosmetics and pharmaceuticals in general.
  
  
  METHODS
  In our prior work, we developed a method that combined a physics-based model of human skin with machine learning and Hyperspectral imaging to non-invasively estimate physiological skin parameters, including melanosomes, collagen, oxygen saturation, and blood volume. In this work, we extend that model to also estimate skin thickness. Moreover, for the first time, we develop a protocol to test our methodology for skin thickness estimation using Ultrasound to acquire a gold standard dataset.
  
  
  RESULTS
  We tested our methodology for skin thickness estimation on a dataset of 48 Hyperspectral signatures obtained in vivo from six patients under IRB at Johns Hopkins Hospital. We found mean absolute errors on the order of the Ultrasound resolution (i.e., our gold standard).
  
  
  CONCLUSIONS
  This is the first study of its kind to validate skin thickness estimates using a gold standard. Our preliminary results constitute a proof-of-concept that hyperspectral-based methods can accurately and non-invasively estimate skin thickness in clinical settings.
authors.affiliation: Applied Physics Laboratory, The Johns Hopkins University, MD, USA; Department of Biomedical Engineering, The Johns Hopkins University, MD, USA.
authors.forename: Saurabh
authors.initials: S
authors.lastname: Vyas
authors.affiliation: Department of Dermatology, The Johns Hopkins University, MD, USA; School of Medicine, The Johns Hopkins University, MD, USA.
authors.forename: Jon
authors.initials: J
authors.lastname: Meyerle
authors.affiliation: Applied Physics Laboratory, The Johns Hopkins University, MD, USA; School of Medicine, The Johns Hopkins University, MD, USA; Department of Computer Science, The Johns Hopkins University, MD, USA.
authors.forename: Philippe
authors.initials: P
authors.lastname: Burlina
mesh_terms: D001185:Artificial Intelligence; D006801:Humans; D008954:Models, Biological; D061848:Optical Imaging / Q000379:methods*; D012044:Regression Analysis; D015203:Reproducibility of Results; D012867:Skin / Q000000981:diagnostic imaging*; D012879:Skin Physiological Phenomena; D014463:Ultrasonography / Q000379:methods
keywords: Hyperspectral signatures; Machine learning regression; Skin thickness; Ultrasound