  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.
