20240049960. PREDICTING CLINICAL PARAMETERS RELATING TO GLAUCOMA FROM CENTRAL VISUAL FIELD PATTERNS simplified abstract (MASSACHUSETTS EYE AND EAR INFIRMARY)
PREDICTING CLINICAL PARAMETERS RELATING TO GLAUCOMA FROM CENTRAL VISUAL FIELD PATTERNS
Organization Name
MASSACHUSETTS EYE AND EAR INFIRMARY
Inventor(s)
PREDICTING CLINICAL PARAMETERS RELATING TO GLAUCOMA FROM CENTRAL VISUAL FIELD PATTERNS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240049960 titled 'PREDICTING CLINICAL PARAMETERS RELATING TO GLAUCOMA FROM CENTRAL VISUAL FIELD PATTERNS
Simplified Explanation
The patent application describes a system and method for predicting clinical parameters related to glaucoma using central visual field patterns. The system includes a processor, an output device, and a computer-readable medium storing executable instructions.
- Pattern decomposition component: Receives visual field data for a patient and decomposes it into a linear combination of patterns defined via archetypal analysis over a corpus of visual field data. This provides a set of decomposition coefficients.
- Machine learning model: Determines a clinical parameter for the patient using at least the set of decomposition coefficients.
- User interface: Provides the determined clinical parameter to a user.
Potential applications of this technology:
- Early detection of glaucoma: By analyzing central visual field patterns, the system can predict clinical parameters related to glaucoma, allowing for early detection and intervention.
- Personalized treatment plans: The system can provide clinicians with valuable information about a patient's condition, enabling them to develop personalized treatment plans based on the predicted clinical parameters.
Problems solved by this technology:
- Limited predictive capabilities: Traditional methods for predicting clinical parameters related to glaucoma may be limited in accuracy. This technology improves prediction accuracy by utilizing pattern decomposition and machine learning techniques.
- Time-consuming analysis: Analyzing visual field data manually can be time-consuming and prone to human error. This technology automates the analysis process, saving time and reducing errors.
Benefits of this technology:
- Improved accuracy: By utilizing pattern decomposition and machine learning, this technology improves the accuracy of predicting clinical parameters related to glaucoma.
- Time-saving: The automated analysis process saves time for clinicians, allowing them to focus on patient care.
- Personalized treatment: The predicted clinical parameters enable personalized treatment plans, leading to better patient outcomes.
Original Abstract Submitted
systems and methods are provided for predicting clinical parameters relating to glaucoma from central visual field patterns. a system includes a processor, an output device, and a computer readable medium that stores executable instructions. the instructions provide a pattern decomposition component that receives a set of visual field data for a patient representing, for each of a plurality of locations in the central region of an eye of the patient, a deviation in sensitivity to a visual stimulus from an age-adjusted normal value and decomposes the set of visual field data into a linear combination of a set of patterns defined via archetypal analysis over a corpus of visual field data to provide a set of decomposition coefficients. a machine learning model determines a clinical parameter for the patient from at least the set of decomposition coefficients, and a user interface provides the determined clinical parameter to a user.
