# Heart DT: Monitoring and Preventing Cardiac Pathologies Using AI and IoT Sensors
## 6 Organ DT Integration for Cardiac Disease Monitoring  
CVDs, such as ACS, are the leading cause of death in the western world and account for the majority of healthcare expenditure due to hospitalization, drugs, and outpatient services. To minimize these costs, the government is promoting digitization in the healthcare sector and DTs constitute a potential solution [40].  
In this context, the PDMP platform could be used in scenarios where it is necessary to both carry out a large-scale monitoring campaign and organize/collect a large amount of data from several acute coronary syndrome (ACS) patients. Another use case is related to the management of the postoperative phase where constant patient monitoring is necessary. It can be managed remotely, reducing the length of stay in the healthcare facility and, at the same time, reducing the posthospitalization costs.  
The authors consider ACS pathology as a relevant case for the implementation of PDMP. In order to model a patient affected by ACS associated with a myocardial infarction, a doctor needs to evaluate information related to the patient's health in a more general context, which takes into account information concerning the condition of other organs, such as the liver and pancreas, in the case the patient is affected by high cholesterol and/or diabetes mellitus, respectively. Moreover, the course of the heart disease requires regular monitoring of the lungs and blood oxygenation.  
For this reason, in addition to the heart DT introduced in Section 3, other organ DTs, including lung DT, have to be implemented in the platform.  
In particular, a lung DT is to be implemented similarly to a heart DT, but the implementation differs mainly in two aspects: the first concerns the nature of the data, which in this case are the saturation values, SpO2, recorded by the smartwatch, while the second difference concerns the AI process block, which no longer implements inference logic for data validation, but threshold algorithms are adopted for the validation of SpO2 values.  
Figure 7 shows the previously introduced elements within the platform.  
The threshold values [41] we have implemented in the Lung DT for classifying SpO2 values are:  
* Threshold 1 [optimal]: SpO2 \(\geq\) 96%;
* Threshold 2 [possible oxygenation problems]: 93% \(\leq\) SpO2 \(\leq\) 96%;
* Threshold 3 [insufficient oxygenation]: 90% \(\leq\) SpO2 \(\leq\) 92%;
* Threshold 4 [severe oxygen deficiency]: SpO2 \(\leq\) 90%.  
In addition to the various organ DTs, according to the PDMP it is necessary to develop an ACS DT which represents the cardiac pathology that affects the patient. The DT block characterizing the ACS DT is the related AI process, which, in this case, implements multivariate AI algorithms considering data of a different nature. Along with the two organ DTs (heart DT and lung DT) and the Pathology DT, in the AI SERVICES layer we have three AI instances, one for each DT, responsible for training and releasing their respective models.  
As shown in Figure 8, the heart DT and the lung DT assess the previously acquired data according to their respective algorithms. Whenever an anomaly is detected, it is notified to the ACS DT. The latter acquires data from the DTs of the connected organs and applies multivariate AI algorithms to assess data by correlating different types of information. Based on the result of this evaluation, the ACS DT determines whether it is necessary to activate an ALERT, such as notifying the doctor, or to continue normal monitoring.