## 2 AI Applications and Research in NAFLD  
### Liver Steatosis  
NAFLD encompasses a wide range of histological conditions, from isolated steatosis to the inflammation stage, and as hepatic fibrosis progresses, it can eventually result in cirrhosis and liver cancer. In recent decades, NAFLD has made its way into the spotlight of serious liver diseases, and is now considered the most common cause of chronic liver disease [1]. The increasing prevalence of metabolic disorders such as obesity, dyslipidemia and type 2 diabetes have made it easier to reach the actual high rates of liver steatosis. In addition to these risk factors, NAFLD has been found to be correlated with genetic factors, intestinal microbiota and diet. However, the knowledge on pathogenesis is still unclear and numerous hypotheses have been proposed. Among them, the \"multiple hit\" hypothesis [5] has been thoroughly investigated, suggesting that various risk factors collaborate to induce changes in the hepatocytes, also explaining why a number of patients with NAFLD do not progress to NASH or other hepatocyte lesions. AI has been increasingly studied for liver steatosis in the past 5 years for the analysis of large amounts of images for the detection and classification of NAFLD. In 2018,  
Figure 3: The structure of a deep neural network (DNN). [PERSON] et al. [6] paved the way for the automatic detection of fatty liver disease, proposing a CNN model aimed at diagnosing hepatic steatosis using B-mode ultrasound images. Their study involved 550 such images of the liver obtained from 55 obese patients who were scheduled to undergo bariatric interventions, 70% being diagnosed with hepatic steatosis. They extracted features that were compatible with steatosis and then used a DL algorithm to classify images, obtaining an overall accuracy of 96.3% compared to the hepato-renal index (HRI) and the grey level coincidence matrix (GLCM). Since then, over 50 studies have identified different AI-assisted methods for the diagnosis or staging of NAFLD. Studies that investigate the performance of DL algorithms in diagnosing hepatic steatosis from US images are relatively heterogenous regarding the AI classifier and feature extraction. An interesting observation is that one study that used a Fuzzy neural network also used advanced image processing methods for improving the image quality, reaching an accuracy of 100%, underlying the importance of feature parameters [7]. However, all the DL models investigated have achieved a very good overall accuracy, exceeding 80% [8; 9; 10; 11; 12; 13]. While the data for an automated diagnosis look promising, there is still a gap for the development of a product to meet the needs of early diagnosis. As for DL algorithms used to categorize the severity of hepatic steatosis, it appears that majority of studies used larger datasets, up to even over 30,000 ultrasound images [14]. Nevertheless, even studies with a lower amount of images analysed have achieved a similar accuracy of about 90% [15]. This may suggest that the accuracy is related to the feature extraction model rather than the AI classifier. Actually, two studies [16; 17] found that ResNet had the highest accuracy compared to other CNN models and entropy imaging. While some studies have found that DL algorithms can differentiate moderate and severe steatosis more efficiently, other classified early or severe steatosis more efficiently [14; 17; 18; 19]. However, all the studies concluded that AI classifiers can be reliably used for quantitative steatosis assessment, similar to or outperforming Fibroscan and the controlled attenuated parameter (CAP), showing a good correlation with MR spectroscopy and medical experts [14; 15; 17; 19; 20]. Moreover, two recent systematic reviews [21; 22] addressed how AI can be integrated for the diagnosis of NAFLD, NASH and liver fibrosis, as well as how it performs on ultrasound images to diagnose and quantify NAFLD.