Development of a novel deep learning method that transforms tabular input variables into images for the prediction of...

Steatotic liver disease (SLD), formerly named fatty liver disease, has a prevalence estimated at 30-38% in adults. Detection of SLD is important, since prompt initiation of treatment can stop disease progression, lead to a reduction in adverse outcomes, and reduce the economic burden associated with the disease. We report the development of a novel Deep Learning (DL) method for the prediction of SLD, which consists of transforming the input variables from tabular data into images, with the goal of using the pattern recognition power of DL models to reach the best prediction performance. The dataset used in this study includes registries from 2,999 patients. The data of each patient, originally represented as a vector, is converted into an image replicating each variable in rows and columns. Our DL models reach better results compared to those of traditional ML models at various levels of sensitivity and specificity. A sensitivity of 0.9497, a specificity of 0.6417, and an AUCROC of 0.8662 were reached with one DL model. We also achieved significantly better results relative to those obtained with the Hepatic Steatosis Index (HSI). Our DL models reach higher AUCROC values compared to those of the traditional ML models, and also with respect to those obtained with HSI.