Department of Dermatology, Seoul National University College of Medicine and Seoul National University Bundang Hospital, Seongnam, Korea.
Br J Dermatol. 2014 Aug;171(2):252-8. doi: 10.1111/bjd.12769. Epub 2014 May 28.
Rosacea and seborrhoeic dermatitis are common diseases that cause facial erythema. They have common features and are frequently misdiagnosed.
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.
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 Lab* 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.
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.
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.
酒渣鼻和脂溢性皮炎是常见的引起面部红斑的疾病。它们有共同的特征，经常被误诊。
通过计算机辅助图像分析（CAIA）提取红斑毛细血管扩张型酒渣鼻（ETR）、丘疹脓疱型酒渣鼻（PPR）和脂溢性皮炎（SEB）的特征，并与主观识别特征进行比较，利用这些发现构建鉴别诊断的决策树。
对 34 例面部红斑患者的临床照片进行评估：12 例患者被归类为 ETR，12 例为 PPR，10 例为 SEB。5 名皮肤科医生对原始诊断不知情，对每张照片进行印象评估。使用 CAIA 计算每张照片红斑参数 a*（Lab色彩空间的 a）的平均值、标准差和 T 区到 U 区（T/U）比值。比较印象组之间的 CAIA 参数。使用受试者工作特征曲线分析确定每种疾病最相关的 CAIA 参数。构建从给定 CAIA 参数预测诊断的决策树。
所有归类为 PPR 的照片都生成了 PPR 的印象。然而，大约 30%归类为 ETR 的照片生成了 SEB 的印象，反之亦然。PPR 的特征是脸颊红斑的标准差较大，ETR 的特征是 U 区红斑的平均值较大，SEB 的特征是平均红斑的 T/U 比值较大。另外检查了 15 张照片：决策树预测了 14 张照片的原始诊断，但错误地预测了一张 ETR 为 SEB。
面部红斑的 CAIA 结果与实际临床诊断密切相关。使用包含 CAIA 参数的决策树进行鉴别诊断的准确性与皮肤科医生的整体检查印象相当。
