Noninvasive Diagnosis of Nonalcoholic Fatty Liver Disease and Quantification of Liver Fat with Radiofrequency Ultrasound Data Using One-dimensional Convolutional Neural Networks

Abstract

N onalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease worldwide, affecting approximately 25% of the human population (1). NAFLD covers a spectrum of liver abnormalities ranging from simple steatosis to nonalcoholic steatohepatitis. Hepatic steatosis, characterized by the accumulation of fat droplets within hepatocytes, can progress to nonalcoholic steatohepatitis, fibrosis, cirrhosis, and even hepatocellular carcinoma (1,2). Early detection and treatment may halt or reverse NAFLD progression (2). Liver biopsy remains the reference standard for diagnosing NALFD and grading hepatic steatosis. However, biopsy is costly, invasive, and inappropriate for screening.There is a critical need to develop noninvasive imaging methods to assess hepatic steatosis. Several modalities have been investigated (3-8), among which MRI and conventional (qualitative) US have the advantage of involving no ionizing radiation. Confounder-corrected chemical shiftencoded MRI can measure the proton density fat fraction (PDFF), a leading method for noninvasive quantification of hepatic steatosis (4,5). However, chemical shift-encoded MRI is not routinely accessible. Conventional US is widely

Leveraging AutoML to provide NAFLD screening diagnosis: Proposed machine learning models

“…Atabaki-Pasdar N et al (11) in a major modelling & validation study concluded that the highest AUC (of 0.84 for the respective study) is obtained by the combination of "-omics" data & clinical variables. Using MRI-derived proton density fat fraction for referencing, Han A et al (12) developed deep learning one-dimensional convolutional neural networks for NAFLD diagnosis by taking in ultrasound data. 1 By taking in all the patients who had been screened for fatty liver at the New Taipei City Hospital between the 1st and 31 st of December 2009, Wu CC et al (13) developed several classification models to predict fatty liver disease and obtained the highest AUC of 0.925 on a Random Forest model.…”

“…For the Han A et al(12) study, the metrics against which the respective proposed model was evaluated did not include AUC.…”

Current status of deep learning applications in abdominal ultrasonography

“…Ten studies applying deep learning to liver US imaging aimed to evaluate diffuse liver disease, especially hepatic fibrosis and steatosis [2][3][4][5][6][7][8][9][10][11]. These studies are summarized in Table 1.…”

Performance of Ultrasound Techniques and the Potential of Artificial Intelligence in the Evaluation of Hepatocellular Carcinoma and Non-Alcoholic Fatty Liver Disease

“…A study by Han et al [ 133 ] sought to evaluate DL algorithms that use radiofrequency (RF) data for NAFLD evaluation, analyzing 204 participants with 140 NAFLD-affected patients. Reference was set with MRI-derived proton density fat fraction (PDFF).…”

“…The Han study showed a high classification accuracy classifier (96%) with an AUROC of 0.98. Moreover, the sensitivity in the RF without time gain compensation was 97% [95% CI: 90–100%] and specificity 94% [95% CI: 79–99%] [ 133 ].…”