Institute of Fundamental Technological Research

Introduction: Nonalcoholic fatty liver disease (NAFLD) is a common liver abnormality, but its non-invasive diagnosis in patients with severe obesity remains difficult.
Objectives: To investigate the usefulness of the ultrasound (US) based hepatorenal index (HRI) technique, and two biomarker-based methods, including the hepatic steatosis index (HSI) and NAFLD logit score for the diagnosis of NAFLD in subjects referred for the bariatric surgery.
Patients and methods: 162 subjects, 106 with NAFLD, admitted for the bariatric surgery participated in the study. Fat fraction level and the presence of NAFLD were determined using surgical liver biopsy. Each patient underwent liver US examination and blood tests to determine the HRI, HSI and NAFLD logit score.
Results: For the NAFLD diagnosis, the HRI, HSI and NAFLD logit score techniques achieved areas under the receiver operating characteristic curves of 0.879, 0.577 and 0.825, respectively. The Spearman’s correlation coefficients between the liver fat fraction values and the HRI, HSI and NAFLD logit score were equal to 0.695, 0.215 and 0.595, respectively. The optimal cut-off values for the NAFLD diagnosis for the HRI, HSI and NAFLD logit score were equal to 1.12, 56.1 and 0.59, and significantly differed from the cut-off values reported for the general population in the literature.
Conclusions: Our study confirms the usefulness of only two out of three techniques, the HRI and the NAFLD logit score for the diagnosis of NAFLD in patients with severe obesity. Methods designed for the general population require different cut-off values to achieve accurate performance in severe obesity.

biomarkers, fatty liver disease, hepatorenal index, obesity, ultrasound

Purpose
The nonalcoholic fatty liver disease is the most common liver abnormality. Up to date, liver biopsy is the reference standard for direct liver steatosis quantification in hepatic tissue samples. In this paper we propose a neural network-based approach for nonalcoholic fatty liver disease assessment in ultrasound.
Methods
We used the Inception-ResNet-v2 deep convolutional neural network pre-trained on the ImageNet dataset to extract high-level features in liver B-mode ultrasound image sequences. The steatosis level of each liver was graded by wedge biopsy. The proposed approach was compared with the hepatorenal index technique and the gray-level co-occurrence matrix algorithm. After the feature extraction, we applied the support vector machine algorithm to classify images containing fatty liver. Based on liver biopsy, the fatty liver was defined to have more than 5% of hepatocytes with steatosis. Next, we used the features and the Lasso regression method to assess the steatosis level.
Results
The area under the receiver operating characteristics curve obtained using the proposed approach was equal to 0.977, being higher than the one obtained with the hepatorenal index method, 0.959, and much higher than in the case of the gray-level co-occurrence matrix algorithm, 0.893. For regression the Spearman correlation coefficients between the steatosis level and the proposed approach, the hepatorenal index and the gray-level co-occurrence matrix algorithm were equal to 0.78, 0.80 and 0.39, respectively.
Conclusions
The proposed approach may help the sonographers automatically diagnose the amount of fat in the liver. The presented approach is efficient and in comparison with other methods does not require the sonographers to select the region of interest.

Nonalcoholic fatty, liver disease, Ultrasound imaging Deep learning, Convolutional neural networks, Hepatorenal index, Transfer learning

Convolutional neural networks (CNNs) have achieved remarkable success in medical image analysis tasks. In ultrasound (US) imaging, CNNs have been applied to object classification, image reconstruction and tissue characterization. However, CNNs can be vulnerable to adversarial attacks, even small perturbations applied to input data may significantly affect model performance and result in wrong output. In this work, we devise a novel adversarial attack, specific to ultrasound (US) imaging. US images are reconstructed based on radio-frequency signals. Since the appearance of US images depends on the applied image reconstruction method, we explore the possibility of fooling deep learning model by perturbing US B-mode image reconstruction method. We apply zeroth order optimization to find small perturbations of image reconstruction parameters, related to attenuation compensation and amplitude compression, which can result in wrong output. We illustrate our approach using a deep learning model developed for fatty liver disease diagnosis, where the proposed adversarial attack achieved success rate of 48%.

adversarial attacks, deep learning, fatty liver, transfer learning, ultrasound imaging