Enhanced Vascular Disease Diagnosis System

An improved system for diagnosing vascular disease, leveraging advanced machine learning models, personalized synthetic models, and real-time user feedback to enhance accuracy, efficiency, and user experience.

The original patent disclosed a method for diagnosing vascular disease using geometric feature parameters and fractional flow reserve data. However, this approach has limitations, such as relying on a single synthetic model and lacking personalized diagnosis capabilities. The new inventive concept addresses these limitations by introducing a combination of geometric feature parameters and biometric authentication data, personalized synthetic models, and multiple machine learning models trained on diverse medical imaging data.

The enhanced vascular disease diagnosis system comprises a processor configured to analyze a combination of geometric feature parameters and biometric authentication data to improve accuracy of diagnosis. The system generates personalized synthetic models based on a patient's medical history to enhance diagnosis efficiency. Additionally, the system includes a database storing multiple machine learning models, each trained on different types of medical imaging data to improve diagnosis accuracy. The system also employs a deep learning algorithm to identify patterns in medical imaging data and predict disease progression. Furthermore, the system features a user interface providing real-time feedback to a user during diagnosis, thereby improving user experience.

The new inventive concept introduces a novel combination of geometric feature parameters and biometric authentication data, as well as personalized synthetic models, which are not disclosed in the original patent. The use of multiple machine learning models trained on diverse medical imaging data and a deep learning algorithm to predict disease progression also represents a significant improvement over the original patent.

Alternative embodiments of the inventive concept could include using different types of machine learning algorithms, such as support vector machines or random forests, or incorporating additional data sources, such as electronic health records or wearable device data. Variations of the system could also be designed for specific types of vascular disease or tailored to specific patient populations.

The enhanced vascular disease diagnosis system has significant commercial potential in the healthcare industry, particularly in the areas of cardiovascular disease diagnosis and treatment. The system could be marketed to hospitals, clinics, and medical research institutions, and could also be integrated into existing medical imaging systems and electronic health record platforms.