## 4 Clinical Applications of AI to CHF Management
### Monitoring Devices and AI-Powered Platforms  
Various devices able to precisely and timely monitor heart failure patients for the early detection of the prediction of clinical worsening are actually available on the market, and many others are under development. Wearable devices that continuously monitor heart rhythm and are already used for detecting atrial fibrillation, such as KardiaMobile by AliveCor [34], and iRhythm Zio XT [35]. In patients with CHF, the impairment of cardiac pumping capacity can cause pulmonary congestion and shortness of breath (dyspnea). This was the drive to develop implantable devices such as CardioMEMS [36] that can measure pulmonary artery pressure (PAP) or devices such as remote dielectric sensing (ReDS) [37] that use electromagnetic waves to detect the extent of pulmonary congestion, which infers lung field concentration, helping in the interpretation of CT scans of lung field concentration. Another device is LINK-HF [38], a non-invasive sensor placed on the patient's chest using adhesive tape able to record ECG, 3-axis accelerometry, skin impedance, body temperature, and posture. Finally, data saved on a cell phone are transferred to an encrypted cloud for viewing and storage. Implantable cardioverter defibrillators offer the opportunity to record and store a number of data which might be well used to increase patients' management. In recent years, a number of algorithms were developed that use data acquired by means of ICD to predict the worsening of HF and the development of acute exacerbations. Among these, the HeartLogic (Boston Scientific, Marlborough, MA, USA), is an automatic, remotely accessible system that combines trend analysis from different sensors, including nocturnal heart rate, acoustic analysis of heart sounds, intraduc-racic impedance, respiratory rate, tidal volume, and physical activity, integrating them to generate a single numerical indicator, the HeartLogic index. A significant alteration of the index suggests an acute decompensation of HF. Particularly, an increase in heart rate and a higher intensity of the third heart sound, a decrease in the first heart sound, a higher respiratory rate, a reduction in breathing depth, a diminishing inspiratory volume, a fall in intrathoracic impedance associated with pulmonary congestion, a lower level of physical activity are hallmarks of HF worsening. The system is able to transfer relevant information to a remote monitoring platform that can be accessed by healthcare personnel. The MultiSENSE (Multisensor Chronic Evaluation in Ambulatory Heart Failure Patients) trial enrolled 900 patients with cardiac resynchronisation therapy defibrillators using the HeartLogic model. The algorithm automatically calculated a daily HF index and identified periods as \"IN\" or \"OUT\" of an active alert state relative to a configurable threshold. This dynamic assessment can identify patients at increased risk of worsening HF and who, among these, could potentially benefit from early treatment [39]. Along the same line, the MANAAGE-HF study enrolled 200 patients implanted with a CRT-D or ICD powered by the HeartLogic algorithm. It demonstrated that the guidance provided by the HeartLogic allowed earlier treatment augmentation, which was then associated with more rapid recovery of the HeartLogic index and of the clinical status of the patient [40]. The utility of the HeartLogic was independently confirmed by [PERSON] et al. The alert system was tested in 104 patients, with the adoption of a standardised protocol including remote data reviews and patient phone contacts every month and at the time of alerts. During a median follow-up of 13 months, the overall number of HF hospitalisations was 16, and 100 alerts were reported in 53 patients. Sixty alerts were judged clinically meaningful and were associated with multiple HF-related clinical conditions [41].