N-of-1 trials are within-person crossover trials allowing both personalized and population-level inference on the effect of health interventions. Using the full potential of modern technologies, multimodal N-of-1 trials can integrate multimedia data for measuring health outcomes. However, methodology required for automated applications in large multimodal trials is not available yet. Here, we present an unsupervised approach for modeling multimodal N-of-1 trials, bypassing the need for expensive outcome labeling by medical experts. First, an autoencoder is trained on the outcome medical images. Then, the dimensionality of embeddings is reduced by extracting the first principal component, which is finally tested for its association with the treatment. Results from imaging simulation studies show high power in detecting a treatment effect while controlling type I error rates. An application to imaging N-of-1 trials of acne severity identifies individual treatment effects and supports that our methodology can enable large clinical multimodal N-of-1 trials.

翻译：N-of-1试验是一种个体内交叉试验设计，能够在健康干预效果评估中实现个性化与群体水平的统计推断。借助现代技术的潜力，多模态N-of-1试验可整合多媒体数据以测量健康结果。然而，目前尚缺乏适用于大规模多模态试验自动化应用的方法学框架。本文提出一种无监督学习方法用于多模态N-of-1试验建模，通过规避医学专家进行昂贵结果标注的需求。首先，在结果医学图像上训练自编码器；随后通过提取第一主成分降低嵌入维度；最终检验该成分与治疗措施的关联性。影像模拟研究结果显示，该方法在控制I类错误率的同时具有较高的治疗效应检测效能。在痤疮严重程度的影像N-of-1试验应用中，该方法成功识别个体化治疗效果，证实本方法学能够支撑大规模临床多模态N-of-1试验的实施。