Cutaneous erythema is used in diagnosis and response assessment of cutaneous chronic graft-versus-host disease (cGVHD). The development of objective erythema evaluation methods remains a challenge. We used a pre-trained neural network to segment cGVHD erythema by detecting changes relative to a patient's registered baseline photo. We fixed this change detection algorithm on human annotations from a single photo pair, by using either a traditional approach or by marking definitely affected ("Do Not Miss", DNM) and definitely unaffected skin ("Do Not Include", DNI). The fixed algorithm was applied to each of the remaining 47 test photo pairs from six follow-up sessions of one patient. We used both the Dice index and the opinion of two board-certified dermatologists to evaluate the algorithm performance. The change detection algorithm correctly assigned 80% of the pixels, regardless of whether it was fixed on traditional (median accuracy: 0.77, interquartile range 0.62-0.87) or DNM/DNI segmentations (0.81, 0.65-0.89). When the algorithm was fixed on markings by different annotators, the DNM/DNI achieved more consistent outputs (median Dice indices: 0.94-0.96) than the traditional method (0.73-0.81). Compared to viewing only rash photos, the addition of baseline photos improved the reliability of dermatologists' scoring. The inter-rater intraclass correlation coefficient increased from 0.19 (95% confidence interval lower bound: 0.06) to 0.51 (lower bound: 0.35). In conclusion, a change detection algorithm accurately assigned erythema in longitudinal photos of cGVHD. The reliability was significantly improved by exclusively using confident human segmentations to fix the algorithm. Baseline photos improved the agreement among two dermatologists in assessing algorithm performance.

中文翻译：

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基线照片和可靠的注释改进了皮肤移植物抗宿主病的自动检测。

皮肤红斑用于皮肤慢性移植物抗宿主病 (cGVHD) 的诊断和反应评估。客观红斑评估方法的发展仍然是一个挑战。我们使用预训练的神经网络通过检测相对于患者注册基线照片的变化来分割 cGVHD 红斑。我们通过使用传统方法或通过标记明确受影响（“不要错过”，DNM）和绝对未受影响的皮肤（“不包括”，DNI），针对来自单个照片对的人类注释修复了这种变化检测算法。固定算法应用于来自一名患者的六个后续会话的其余 47 个测试照片对中的每一个。我们同时使用 Dice 指数和两位经过董事会认证的皮肤科医生的意见来评估算法性能。变化检测算法正确分配了 80% 的像素，无论它是固定在传统（中位数精度：0.77，四分位距 0.62-0.87）还是 DNM/DNI 分割（0.81，0.65-0.89）上。当算法固定在不同注释者的标记上时，DNM/DNI 实现了比传统方法 (0.73-0.81) 更一致的输出（中值 Dice 指数：0.94-0.96）。与仅查看皮疹照片相比，添加基线照片提高了皮肤科医生评分的可靠性。评分者间组内相关系数从 0.19（95% 置信区间下限：0.06）增加到 0.51（下限：0.35）。总之，变化检测算法准确地分配了 cGVHD 纵向照片中的红斑。通过专门使用自信的人类分割来修复算法，可靠性得到了显着提高。基线照片提高了两位皮肤科医生在评估算法性能方面的一致性。