Quantifying skin photodamage with spatial frequency domain imaging: statistical results

Abstract

We investigated the change in optical properties and vascular parameters to characterize skin tissue from mild photodamage to actinic keratosis (AK) with comparison to a published photodamage scale. Multi-wavelength spatial frequency domain imaging (SFDI) measurements were performed on the dorsal forearms of 55 adult subjects with various amounts of photodamage. Dermatologists rated the levels of photodamage based upon the photographs in blinded fashion to allow comparison with SFDI data. For characterization of statistical data, we used artificial neural networks. Our results indicate that optical and vascular parameters can be used to quantify photodamage and can discriminate between the stages as low, medium, and high grades, with the best performance of ∼70%, ∼76% and 80% for characterization of low-medium-and high-grade lesions, respectively. Ultimately, clinicians can use this noninvasive approach for risk assessment and frequent monitoring of high-risk populations.

Inter- and Intra-Physician Variation in Quantifying Actinic Keratosis Skin Photodamage

“…In addition to the standard 10 levels of FFPAS, the authors further down sampled the ratings into 3 groups, the mild, moderate, and severe high level groups of [6] as used in the prior work of [7]. When considering the down sampled groups, we found moderate interrater agreement and substantial intrarater agreement, both an improvement over the 10 levels of FFPAS.…”

“…Notionally, these 3 down sampled groups followed the general groupings of Table 1, where scores of 0, 1, 2, and 3 are mild, scores of 4, 5, or 6 are moderate, and scores of 7, 8, and 9 are severe. Consistent with [7], scores of 0 are grouped into mild due there being few observations of 0 in the study. These groups were in the implicit groupings of Table 1 and were used in [7] to develop a three-class machine learning classifier for actinic damage classification.…”

“…Consistent with [7], scores of 0 are grouped into mild due there being few observations of 0 in the study. These groups were in the implicit groupings of Table 1 and were used in [7] to develop a three-class machine learning classifier for actinic damage classification. This down sampled FFPAS scores are used herein to understand how different multitudes of ratings might affect rater reliability.…”

“…Travers et al [7] down sampled, or pooled, the 10 FFPAS categories into three groups (Mild/none, Moderate, and Severe). Notionally, these 3 down sampled groups followed the general groupings of Table 1, where scores of 0, 1, 2, and 3 are mild, scores of 4, 5, or 6 are moderate, and scores of 7, 8, and 9 are severe.…”

“…Recent studies have suggested that noninvasive imaging of skin could detect and monitor precancerous lesions using hemoglobin contrast [7]. However, complicating both manual and computer detection of AKs, is the complexity of the FFPAS evaluation mechanism.…”