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  • Title: Characteristics of subjective recognition and computer-aided image analysis of facial erythematous skin diseases: a cornerstone of automated diagnosis.
    Author: Choi JW, Kim BR, Lee HS, Youn SW.
    Journal: Br J Dermatol; 2014 Aug; 171(2):252-8. PubMed ID: 24354615.
    Abstract:
    BACKGROUND: Rosacea and seborrhoeic dermatitis are common diseases that cause facial erythema. They have common features and are frequently misdiagnosed. OBJECTIVES: To extract characteristic features of erythrotelangiectatic rosacea (ETR), papulopustular rosacea (PPR) and seborrhoeic dermatitis (SEB) through computer-aided image analysis (CAIA) and compare them with subjectively recognized features and to use these findings to construct a decision tree for differential diagnosis. METHODS: Thirty-four clinical photos of patients with facial erythema were assessed: 12 patients were classified as showing ETR, 12 as PPR and 10 as SEB. Five dermatologists blinded to the original diagnosis gave their impressions of each photo. The mean, SD and T-zone to U-zone (T/U) ratios of the erythema parameter a* (a* of the L*a*b* colour space) were calculated for each photo using CAIA. These CAIA parameters were compared between impression groups. The most closely related CAIA parameter for each disease was established using the receiver-operating characteristic curve analysis. A decision tree which predicts the diagnosis from given CAIA parameters was constructed. RESULTS: All the photos classified as PPR generated impressions of PPR. However, approximately 30% of the photos classified as ETR generated impressions of SEB and vice versa. PPR was characterized by a large SD of erythema of the cheek, ETR was characterized by a large mean erythema of the U-zone, and SEB was characterized by a large T/U ratio of mean erythema. Fifteen additional photos were examined: the decision tree predicted the original diagnosis for 14, but incorrectly predicted one case of ETR as SEB. CONCLUSIONS: The CAIA result of facial erythema is well correlated with the actual clinical diagnosis. The accuracy of differential diagnosis using a decision tree with CAIA parameters is as good as that of global examination impressions of dermatologists.
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