18263503. SYSTEMS AND METHODS FOR DIAGNOSING NEURODEGENERATIVE DISEASES simplified abstract (Arizona Board of Regents on Behalf of Arizona State University)
Contents
- 1 SYSTEMS AND METHODS FOR DIAGNOSING NEURODEGENERATIVE DISEASES
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SYSTEMS AND METHODS FOR DIAGNOSING NEURODEGENERATIVE DISEASES - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
SYSTEMS AND METHODS FOR DIAGNOSING NEURODEGENERATIVE DISEASES
Organization Name
Arizona Board of Regents on Behalf of Arizona State University
Inventor(s)
Carol J. Huseby of Tempe AZ (US)
Paul Coleman of Fountain Hills AZ (US)
SYSTEMS AND METHODS FOR DIAGNOSING NEURODEGENERATIVE DISEASES - A simplified explanation of the abstract
This abstract first appeared for US patent application 18263503 titled 'SYSTEMS AND METHODS FOR DIAGNOSING NEURODEGENERATIVE DISEASES
Simplified Explanation
The abstract describes a patent application for a machine learning model that can select transcripts in blood to distinguish neurodegenerative diseases based on mRNA gene expression changes.
- The processor is configured to implement a machine learning model.
- The model is trained to select transcripts in blood for distinguishing neurodegenerative diseases.
- The algorithm leverages blood-based changes in mRNA gene expression.
- The goal is to differentiate patients with any neurodegenerative disease, regardless of specific proteins or post-translational modifications.
Potential Applications
This technology could be applied in medical diagnostics to aid in the early detection and differentiation of neurodegenerative diseases.
Problems Solved
This technology addresses the challenge of accurately diagnosing neurodegenerative diseases using blood-based biomarkers, which can be less invasive and more accessible than traditional methods.
Benefits
The benefits of this technology include early detection of neurodegenerative diseases, personalized treatment plans, and potentially improved patient outcomes.
Potential Commercial Applications
One potential commercial application of this technology could be the development of a diagnostic tool for healthcare providers to use in identifying neurodegenerative diseases.
Possible Prior Art
Prior research may exist on using machine learning models for analyzing gene expression patterns in blood samples to diagnose various diseases.
What are the limitations of this technology in real-world applications?
This article does not address the scalability of the technology for widespread use in clinical settings or potential challenges in integrating it into existing diagnostic workflows.
How does this technology compare to traditional methods of diagnosing neurodegenerative diseases?
This article does not provide a direct comparison between the accuracy, cost-effectiveness, or efficiency of this technology compared to traditional diagnostic methods like imaging or cerebrospinal fluid analysis.
Original Abstract Submitted
A processor is configured to implement a machine learning model that is trained to select transcripts in blood for distinguishing neurodegenerative diseases. The algorithm is developed via machine learning and leverages concepts associated with blood-based changes in mRNA gene expression for differentiating patients of any neurodegenerative disease regardless of the proteins or their post-translational modifications occurring in disease.
