## 4 Clinical Applications of AI to CHF Management
### Application of AI to Echocardiography, ECG, and Cardiac MRI  
Applications of AI techniques to electrocardiography, echocardiography, and electronic health records are the most promising. Some experience is already available with CHF patients. The use of AI in echocardiography has been shown to have the potential to mitigate common limitations of this diagnostic technique, such as long execution times with manual measurement of multiple parameters, a high operator subjectivity causing wide observation ranges, and systematic bias [56]. To address these issues, ML models have been trained to recognise specific echocardiographic markers of a wide variety of cardiac diseases helping in the interpretation of data and leading to faster analysis and better outcomes. DL has been mostly utilised in imaging for segmenting the ventricles and  
Figure 2: Potential applications of AI-tools to telemedicine. Exemplification case of use of AI-powered tools for the management of HF patients outside the hospital. evaluating left ventricular ejection fraction (EF), left ventricular volume, left ventricular wall motion function, myocardial contractility, and global longitudinal strain (GLS) [57]. Another good example of the application of DL to echocardiography is the possibility to automatically annotate and classify both 2D and Doppler tracings with excellent performance (AUC = 0.90 -- 0.92 to detect a left ventricular ejection fraction) [58]. Another use of ML in this field is with cardiac MRIs, generating segmentations of heart chambers that yield imaging biomarkers to predict CHF with an excellent reconstruction accuracy both for the right ventricle and for calculation of left ventricular mass, papillary muscle identification, common carotid artery, and descending aorta measurements [16]. Interpretation of multiple features on ECG tracings also presents some hurdles. In this regard, an AI-powered platform for the analysis and interpretation of ECG data, thanks to ML models, has recently been shown to help reduce the time taken in diagnosis to recognise the patient to send to urgent care quickly. A similar AI tool enables the use of ECG as a screening tool (e.g., to predict heart failure in asymptomatic individuals or the worsening in patients with an established diagnosis) learning from big data sets, without the need to understand the biological mechanism [59]. An example is Cardiogram's DeepHeart platform [60], an AI-powered platform that uses machine learning algorithms to detect ischemic heart failure from ECG data. Artificial-intelligence-powered algorithms can be very practical in analysing long EKG recordings, which would otherwise be very demanding. In fact, the improvements in wearable technologies make it nowadays possible to register continuous EKG tracings for several days up to some weeks [61]. Without an AI-based analysis, it would not be possible for healthcare personnel to analyse the complete tracings [62; 63]. [PERSON] et al. demonstrated an excellent predictive capacity (AUC = 0.903) using electrocardiograms to detect left ventricular systolic dysfunction across age and ethnic subgroups [64]. Cardio-HART is an AI-powered, cardiac diagnostic system for use in clinical care settings, including primary and secondary care. It starts in a clinical care setting where the CHART device first captures the biosignals, ECG, phonocardiography (PCG), and mechanical force bio-signal (MCG), and then uploads them to the cloud for AI processing [65]. The CHART AI then outputs a wide range of medical findings or endpoints, consistent with cardiac dysfunction. In particular, Cardio-HART can diagnose 14 HART-findings--including structural, functional, and valve problems, that are classified as \"Normal/Mild/Abnormal\". From these HART findings, HF is then classified into four phenotypes, consistent with their medical context: HF unlikely, HFpEE, HFmrEF, and HFrEF. HART-findings were validated with a database having both parallel bio-signals and ECHO assessment.