INTRODUCTION

Chronic graft-versus-host disease (cGVHD) is the leading cause of non-relapse long-term morbidity and mortality in patients after allogeneic haematopoietic cell transplantation (HCT).¹ Skin is the earliest and most commonly affected organ and has a central role in evaluating treatment efficacy and disease progression.² By way of National Institutes of Health (NIH) scoring, the affected cutaneous body surface area (BSA) has been incorporated into study design to test all three current FDA-approved cGVHD treatments. For example, one inclusion criterion for the pivotal trial of ibrutinib was a minimum of 25% BSA erythema.³ However, visual assessment of cGVHD suffers low reliability, limiting the ability to effectively follow patient response. The gap of measuring affected BSA in known cGVHD patients could be addressed by automated image analysis techniques leveraging artificial intelligence (AI). To this end, we trained a deep learning neural network⁴ to mark (segment) affected skin and tested performance in 36 previously unseen patients by leave-one-patient-out validation. We further benchmarked the AI against exact human measurements of affected BSA in 3D photographs.

中文翻译：

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基于深度学习神经网络的皮肤慢性移植物抗宿主病人工智能识别

介绍

慢性移植物抗宿主病 (cGVHD) 是异基因造血细胞移植 (HCT) 后患者非复发性长期发病率和死亡率的主要原因。¹皮肤是最早和最常受影响的器官，在评估治疗效果和疾病进展方面起着核心作用。²通过美国国立卫生研究院 (NIH) 评分，受影响的皮肤体表面积 (BSA) 已纳入研究设计，以测试目前 FDA 批准的所有三种 cGVHD 治疗方法。例如，伊布替尼关键试验的一项纳入标准是至少 25% 的 BSA 红斑。^3个然而，cGVHD 的视觉评估可靠性低，限制了有效跟踪患者反应的能力。在已知 cGVHD 患者中测量受影响的 BSA 的差距可以通过利用人工智能 (AI) 的自动图像分析技术来解决。为此，我们训练了一个深度学习神经网络⁴来标记（分割）受影响的皮肤，并通过留一患者验证来测试 36 名以前未见过的患者的表现。我们进一步将 AI 与 3D 照片中受影响的 BSA 的精确人类测量值进行了基准比较。