## 2 AI Applications and Research in NAFLD
### Liver Tumours  
Approximately 25% of patients with NAFLD may develop steatohepatitisitis (NASH), which is further associated with other complications of liver cirrhosis (hepatic encephalopathy, ascites and hepato-renal syndrome, upper gastrointestinal bleeding, etc.) and development of HCC. The prognosis of HCC is considered dismal as it represents the 2 nd leading cause of cancer-related death worldwide. A particular feature of NAFLD-associated HCC is that it involves a higher risk of non-cirrhotic HCC compared to other chronic liver diseases [63], accounting for about 25-45% of the total cases of HCC in NAFLD patients [64]. The epidemiology for NAFLD-associated HCC is similar to NAFLD cirrhosis, with data from the first decade of this century indicating that NASH has become the second most common cause of HCC leading to liver transplants in Unites States [65].  
A recent clinical practice update from the American Gastroenterological Association addressed the topic of surveillance of NAFLD for HCC, including optimal screening tools, frequency of monitoring and the presence of risk factors [66]. An interesting approach was to develop a SWOT (strengths, weaknesses, opportunities and threats) analysis in order to identify non-invasive tests used in NAFLD to select high-risk patients for NASH. The facilitates the surveillance of HCC in order for early diagnosis and to possibly treat and assess the effectiveness of interventions [67].  
An initial systematic review focused on medical imaging and artificial intelligence identified 11 initial papers that studied the differential diagnosis of focal liver lesions (FLL),with the aim of diagnosing, segmenting or differentiating HCC based on various CNN techniques [68]. Whilst conventional ANNs were scarcely used initially [69; 70; 71], it soon became clear that a more structured approach using DL and CNNs would be needed for automatic recognition of HCC areas in ultrasound images [72].  
A recent review analysed the value of AI and DL for the diagnosis, prognosis and therapy of HCC [73]. Newer techniques integrate sets of textured features into ensemble models further analysed by CNN to differentiate normal liver, chronic hepatitis, liver cirrhosis and HCC with an overall accuracy of 96.6% [74]. Furthermore, real-time systems based on full-length ultrasound movies have recently been developed, achieving an overall detection rate of 89.8%, significantly higher than that achieved by non-radiologist physicians and radiologists [75].  
Nevertheless, for correct assessment, contrast-enhanced ultrasound (CEUS) should be employed, as this has better accuracy than greyscale ultrasound imaging alone [76]. Quantitative analysis approaches have been developed recently to avoid errors induced by motion artifacts and the dynamic changes in contrast enhancement during different vascular phases (arterial, portal and venous), achieving an accuracy of 0.84 in the distinction between benign and malignant phases [77]. Similar DL approaches are useful for the differential diagnosis of focal nodular hyperplasia and atypical HCC [78] or for the differential diagnosis of HCC and intrahepatic cholangiocarcinoma [79]. Last, but not least, a similar approach has been employed in the evaluation of patients after therapy, either after transarterial chemoembolization, microwave/radiofrequency ablation or surgery [80; 81].