Skin lesion metrics: role of photography in acne

• Published in: The Journal of dermatological treatment Vol. 25; no. 2; pp. 100 - 105
• Main Authors: Chiang, Audris, Hafeez, Farhaan, Maibach, Howard I.
• Format: Journal Article
• Language: English
• Published: Oslo Informa Healthcare USA on behalf of Informa UK Ltd 01.04.2014; Taylor & Francis
• Subjects: Acne Vulgaris - diagnosis; Biological and medical sciences; Dermatology; Fluorescence; grading; Humans; Medical sciences; photo; Photography - methods; Photography - standards; severity; Skin involvement in other diseases. Miscellaneous. General aspects; Photography; severity; Skin disease; grading; Acne; photo; Standards
• ISSN: 0954-6634; 1471-1753
• DOI: 10.3109/09546634.2013.813010

Accurate assessment of acne severity is essential for determining the appropriate treatment required. This paper reviews photographic methods for such assessment. Literature included met the following criteria: proposed photographic standards to assess acne, evaluated such standards, or offered photographic methods to improve visualization and assessment. Validity was evaluated by comparing given photographic grading methods to other methods, such as lesion counting. Many photographic standards were shown to be objective, time-efficient, and have intra-grader and inter-grader consistency. Photography also documents progress for retrospective verification. Drawbacks include not allowing determination of depth, minimization of small lesions and erythema, and difficulty in maintaining consistent settings. Fluorescence and polarized photography improve visualization beyond clinical observation alone. Advances such as computer alignment, imaging segmentation, and three-dimensional analysis methods track lesions and measure objective characteristics. The combined experience summarized here strongly promotes the use of a photographic standard in assessing acne severity. Cook's method can also be used to train and qualify graders. Photographic advancements improve accuracy of assessment by solving problems with consistent settings and depth visualization. Further advancements can improve image analysis through analysis of objective attributes.