## 2 AI Applications and Research in NAFLD
### Liver Cirrhosis  
For a long time, the leading cause of cirrhosis worldwide was viral hepatitis, but NASH and alcoholic fatty liver disease (AFLD) are soon expected to surpass it as the primary causes of cirrhosis [46; 47]. Currently, the prevalence of NAFLD-associated cirrhosis in the United States is relatively low, at around 1-2% [48], but screening for chronic liver disease in NAFLD high-risk groups is a key strategy for HCC surveillance programs. The importance of elastography for the early identification of patients with liver cirrhosis has been highlighted in a clinical guideline accompanied by a technical review of the American Gastroenterology Association (AGA) [49; 50]. Thus, non-invasive imaging modalities for the evaluation of chronic liver diseases (chronic hepatitis B and/or C, NAFLD or AFLD) prioritised VCTE and MRI elastography looking at (1) the diagnostic performance of VCTE and MRE relative to non-proprietary, serum-based fibrosis markers for the detection of cirrhosis in patients with chronic liver disease mentioned above; (2) the performance of specific VCTE-defined liver stiffness cut-offs as a test replacement strategy for liver biopsies in establishing clinical decision-making algorithms for these patients; and (3) the performance of specific VCTE-defined liver stiffness cut-offs as a triage test to identify patients with a low likelihood of high-risk oesophageal varices (EVs) or having clinically significant portal hypertension (for pre-surgical risk stratification). Meanwhile, other non-invasive modalities for the assessment of fibrosis (e.g., RTE, ARFI or pSWE/2D-SWE) or steatosis (CAP or MRI--estimated proton density fat fraction) have been developed and will certainly be used in clinical decision-making practice algorithms [51]. Thus, according to the latest data, a \"rule of five\" for LSMs with VCTE and a \"rule of four\" for LSMs with the ARFI-based techniques have been proposed to discriminate various fibrosis stages. Furthermore, in patients with advanced CLD, the risk of liver decompensation increases with an increasing liver stiffness value, whilst SWE has been proposed as a risk predictor of morbidity and mortality in patients with cirrhosis. Both VCTE and ARFI techniques have already been validated as non-invasive methods for the screening of varices in this setting. Even more, LSM measurements were part of predictive algorithms for HCC occurrence in this high-risk population with advanced chronic liver diseases. For clinical validation, the AGA studied patients with NAFLD and predicted advanced liver fibrosis [52]. Thus, patients aged over 18 with a Fibrosis-4 (FIB-4) score of less than 1.3 and an LSM of <8 kilopascals (kPa) by VCTE have a low risk of significant fibrosis, although other strategies were deemed necessary to predict patients with advanced fibrosis. Based on all these endeavours and the plethora of non-invasive markers used for risk prediction in chronic liver diseases, it is obvious that AI and DL/ML techniques will be the future avenue for assessment of these patients. Conventional approaches based on ANNs and parenchymal echo patterns of greyscale imaging were already employed even 25 years ago in order to better assess chronic hepatitis and regenerative nodule characteristics of liver cirrhosis [53]. The same group employed a similar methodology to assess the risk of HCC in liver cirrhosis patients [54; 55]. The next step was to analyse the Doppler information yielded by colour flow and pulsed Doppler measurements using an ANN [56]. Thus, several US parameters (liver parenchyma, thickness of spleen, hepatic vein waveform, hepatic artery pulsatile index and hepatic vein damping index) were used to establish an ANN model capable of quantifying advanced liver fibrosis or cirrhosis. ANNs including several serological tests (either alone or combined into scores) and liver stiffness measurements based on VCTE were further analysed for the diagnosis of cirrhosis and significant portal hypertension and oesophageal varices (with an estimated accuracy of over 80%) [57].