US20240347190
2024-10-17
Physics
G16H40/67
A mask-based diagnostic (MBD) system enables remote patient monitoring by collecting both chemical biomarker data and non-chemical biometric data non-invasively. This system is particularly useful for monitoring various medical conditions, including cardiovascular diseases, lung cancer, diabetes, and respiratory diseases. The MBD features an exhaled breath condensate (EBC) collector that analyzes chemical biomarkers in exhaled breath, while additional biometric data such as temperature, heart rate, and blood oxygen levels are gathered through wearable devices.
The collected data from the MBD and biometric sensors is transmitted wirelessly to a remote server. Here, the data undergoes aggregation and analysis using artificial intelligence (AI) algorithms. These algorithms identify patterns and trends within the patient data, which can facilitate drug discovery, health issue identification, and treatment plan adjustments. By leveraging real-time monitoring capabilities, the system aims to enhance patient outcomes through early detection of health issues and personalized treatment strategies.
Current diagnostic methods for diseases such as COVID-19 often involve invasive procedures like nasopharyngeal swabs, which can be uncomfortable and require trained personnel. Saliva-based testing has emerged as a less invasive alternative but presents challenges in sample collection and analysis due to low biomarker concentrations. MBD systems address these issues by allowing patients to collect samples from their breath easily, thus facilitating simpler testing processes that can effectively identify pathogens.
The MBD system incorporates advanced analytical techniques such as unsupervised machine learning algorithms to categorize patients based on their biomarker data. This method helps in identifying clusters of patients with similar health indicators. Additionally, principal component analysis (PCA) is utilized to streamline the data analysis process by reducing its complexity while maintaining essential information about patient conditions.
The integration of AI with MBD technology not only enhances the accuracy of diagnostics but also allows for predictive analytics regarding future cardiac events. By analyzing biomarker data through AI agents like recurrent neural networks (RNNs), the system can provide personalized alerts for early intervention. This innovative approach aims to significantly improve patient outcomes by enabling proactive healthcare measures tailored to individual needs.