Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) · 2011
Acne analysis, grading and computational assessment methods: an overview.
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Ramli Roshaslinie, Malik Aamir Saeed, Hani Ahmad Fadzil Mohamad, Jamil Adawiyah
Аннотация
INTRODUCTION: This paper presents a comprehensive review of acne grading and measurement. Acne is a chronic disorder of the pilosebaceous units, with excess sebum production, follicular epidermal hyperproliferation, inflammation and Propionibacterium acnes activity. Most patients are affected with acne vulgaris, which is the prevalent type of acne. Acne vulgaris consists of comedones (whitehead and blackhead), papules, pustules, nodules and cysts.
OBJECTIVES: To review and identify the issues for acne vulgaris grading and computational assessment methods. To determine the future direction for addressing the identified issues.
METHODS: There are two main methods of assessment for acne severity grading, namely, lesion counting and comparison of patient with a photographic standard. For the computational assessment method, the emphasis is on computational imaging techniques.
RESULTS: Current acne grading methods are very time consuming and tedious. Generally, they rely on approximation for counting lesions and hence the assessment is quite subjective, with both inter and intra-observer variability. It is important to accurately assess acne grade to evaluate its severity as this influences treatment selection and assessment of response to therapy. This will further help in better disease management and more efficacious treatment.
CONCLUSION: Semi-automated or automated methods based on computational imaging techniques should be devised for acne grade assessment.
OBJECTIVES: To review and identify the issues for acne vulgaris grading and computational assessment methods. To determine the future direction for addressing the identified issues.
METHODS: There are two main methods of assessment for acne severity grading, namely, lesion counting and comparison of patient with a photographic standard. For the computational assessment method, the emphasis is on computational imaging techniques.
RESULTS: Current acne grading methods are very time consuming and tedious. Generally, they rely on approximation for counting lesions and hence the assessment is quite subjective, with both inter and intra-observer variability. It is important to accurately assess acne grade to evaluate its severity as this influences treatment selection and assessment of response to therapy. This will further help in better disease management and more efficacious treatment.
CONCLUSION: Semi-automated or automated methods based on computational imaging techniques should be devised for acne grade assessment.