MASK-BASED DIAGNOSTIC UTILIZING AI ALGORITHMS FOR IMPROVED PATIENT OUTCOMES

Smart overview of the Invention

A mask-based diagnostic (MBD) system enables non-invasive remote patient monitoring by collecting both chemical biomarker data and non-chemical biometric data. This system is particularly useful for tracking various medical conditions such as cardiovascular disease, lung cancer, diabetes, and respiratory diseases. It features a mask equipped with an exhaled breath condensate (EBC) collector that tests for specific chemical biomarkers, while wearable devices capture additional biometric data like temperature, heart rate, and blood oxygen levels.

Data Collection and Transmission

The MBD system gathers patient data through the mask and wearable sensors, which is then wirelessly transmitted to a remote server. This server aggregates the collected data for further analysis. The integration of artificial intelligence (AI) algorithms allows for the identification of patterns and trends within the patient data, facilitating drug discovery and enabling personalized treatment adjustments based on real-time monitoring.

Challenges in Biomarker Detection

Collecting and analyzing EBC samples presents several challenges due to low sample volumes and concentrations of biomarkers. Existing diagnostic methods often require invasive procedures or specialized personnel, which can be uncomfortable for patients. The MBD system addresses these issues by providing a simpler, less invasive method for collecting biological samples directly from exhaled breath.

AI-Driven Analysis and Patient Clustering

The system employs unsupervised machine learning algorithms to analyze aggregated data from multiple patients. By identifying clusters of patients with similar cardiac biomarker patterns, healthcare providers can gain insights into patient health trends. Techniques such as principal component analysis (PCA) further refine this data to enhance understanding and improve diagnostic accuracy.

Future Applications and Patient Outcomes

A key aspect of the MBD system is its predictive capability regarding future cardiac events. The device communicates with a smartphone app that processes biomarker data using AI agents trained to forecast potential health issues. By providing personalized alerts to patients or healthcare providers, the system aims to facilitate early interventions, ultimately leading to improved patient outcomes through timely medical responses.