View source for 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==

[[:Category:MASSACHUSETTS EYE AND EAR INFIRMARY|MASSACHUSETTS EYE AND EAR INFIRMARY]]


[[Category:MASSACHUSETTS EYE AND EAR INFIRMARY]]

==Inventor(s)==

[[:Category:Mengyu Wang of Quincy MA (US)|Mengyu Wang of Quincy MA (US)]][[Category:Mengyu Wang of Quincy MA (US)]]

[[:Category:Lucy Shen of Newton MA (US)|Lucy Shen of Newton MA (US)]][[Category:Lucy Shen of Newton MA (US)]]

[[:Category:Tobias Elze of Boston MA (US)|Tobias Elze of Boston MA (US)]][[Category:Tobias Elze of Boston MA (US)]]

==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.



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