# Heart DT: Monitoring and Preventing Cardiac Pathologies Using AI and IoT Sensors
## 5 Organ DT Integration for Pathology Monitoring  
As previously stated, DTs can virtualize the doctor-patient relationship, allowing remote patient monitoring using real-time data and artificial intelligence to predict the course of the disease. The approach discussed in Section 3 can be replicated to implement other organs as well, as suggested by [PERSON] et al. [39]. The implementation of a DT for each organ would provide the healthcare industry with additional tools for monitoring a pathology that involves the analysis and correlation of physiopathological information related to multiple organs of a single patient. Furthermore, the DTs placed in a single platform can communicate with each other and exchange information concerning a particular pathology: if necessary, DTs may be aggregated differently to tackle the particular pathology. Figure 6 shows the architecture of the suggested platform. The platform, hereinafter referred to as pathology DTs monitoring platform (PDMP), introduces a new type of a DT that is responsible for collecting and analyzing physiological data from various organs in order to represent a specific pathology, the pathology DT. In the Figure 6, the physical layer (PL) includes all healthcare data-acquisition devices, such as blood pressure monitors, heart rate monitors, ECG systems, digital medical records, etc. Moreover, it also includes devices that allow for active interaction with the surrounding physical environment, such as, for example, insulin pump systems. Figure 5: Classification accuracy in the “Normal” class for _Patient A_ and _Patient B_ as the analysis window changes. The virtual layer (VL) is composed of all the hardware and software components that create the digital twin of the object in question. Two macro objects can be distinguished:  
* Patients: As previously introduced, a patient is represented by various organ DTs that cooperate with each other and exchange information with the pathology DTs. The organ DT has full control over its corresponding real organ. Its main tasks are both to predict abnormal behaviors that the real organ may encounter and to act on the organ by commanding the related actuators. On the other hand, the pathology DT, having a broader perspective and obtaining more information from individual organ DTs, can operate at a higher level by monitoring specific diseases and preventing future problems. They do not directly act on the real world but rather contact organ DTs to make changes in the physical world. Both types of DTs require the presence of an AI component, residing in the AI process block, for efficient monitoring. Many approaches, such as CNN algorithms, require AI to be trained on specific datasets to provide the most robust and accurate model possible. AI techniques, including dataset training and model creation, are performed in the AI services to achieve greater platform flexibility. * AI services: correspond to the section where different AI algorithms reside. This includes several instances, as each AI may require different datasets and processing techniques. The idea is to have many active AI algorithms, which, after completing all the training phases, can return the models. Whenever the organ DTs and pathology DTs need to perform a data-validation process, such as through an inference process, the AI process block downloads the appropriate previously trained model from the reference AI instance. In this way, the data insertion, dataset training, and real-time data-verification phases are completed independently of each other. If the dataset is increased/varied with new data, a neural network retraining is performed within the AI instance. This operation can be carried out without interrupting the execution of the organ DTs and pathology DTs, which continue to monitor the patient. In addition to the macro objects, we also determine all the interfaces that are used for communication between the various objects present on the PDMP.