Non alcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease, which can be predicted accurately to prevent advanced fibrosis and cirrhosis. While, a liver biopsy, the gold standard for NAFLD diagnosis, is invasive, expensive, and prone to sampling errors. Therefore, non-invasive studies are extremely promising, yet they are still in their infancy due to the lack of comprehensive research data and intelligent methods for multi-modal data. This paper proposes a NAFLD diagnosis system (DeepFLDDiag) combining a comprehensive clinical dataset (FLDData) and a multi-modal learning based NAFLD prediction method (DeepFLD). The dataset includes over 6000 participants physical examinations, laboratory and imaging studies, extensive questionnaires, and facial images of partial participants, which is comprehensive and valuable for clinical studies. From the dataset, we quantitatively analyze and select clinical metadata that most contribute to NAFLD prediction. Furthermore, the proposed DeepFLD, a deep neural network model designed to predict NAFLD using multi-modal input, including metadata and facial images, outperforms the approach that only uses metadata. Satisfactory performance is also verified on other unseen datasets. Inspiringly, DeepFLD can achieve competitive results using only facial images as input rather than metadata, paving the way for a more robust and simpler non-invasive NAFLD diagnosis.

翻译：非酒精性脂肪性肝病（NAFLD）是慢性肝病最常见的原因，准确预测该疾病可预防晚期纤维化和肝硬化。然而，作为NAFLD诊断金标准的肝活检具有侵入性、成本高昂且易出现取样误差。因此，非侵入性研究极具前景，但由于缺乏多模态数据的综合研究数据和智能方法，仍处于初级阶段。本文提出了一种结合综合性临床数据集（FLDData）与基于多模态学习的NAFLD预测方法（DeepFLD）的NAFLD诊断系统（DeepFLDDiag）。该数据集包含6000余名参与者的体格检查、实验室及影像学检查、大量问卷调查及部分参与者面部图像，对临床研究具有全面性和重要价值。我们基于该数据集定量分析并筛选出对NAFLD预测贡献最大的临床元数据。此外，本文提出的DeepFLD——一种专为利用元数据和面部图像等多模态输入预测NAFLD而设计的深度神经网络模型，其性能优于仅使用元数据的方法。该模型在其他未见数据集上也获得了令人满意的验证效果。令人振奋的是，DeepFLD仅需将面部图像作为输入（无需元数据）即可取得具有竞争力的结果，为更稳健、更简便的非侵入性NAFLD诊断开辟了新途径。