20240056749. PREDICTING REAL-EAR-TO-COUPLER DIFFERENCES BASED ON CLINICAL IMMITTANCE MEASURES OF THE MIDDLE EAR simplified abstract (Father Flanagan's Boys' Home Doing Business as Boys Town National Research Hospital)
Contents
PREDICTING REAL-EAR-TO-COUPLER DIFFERENCES BASED ON CLINICAL IMMITTANCE MEASURES OF THE MIDDLE EAR
Organization Name
Father Flanagan's Boys' Home Doing Business as Boys Town National Research Hospital
Inventor(s)
Ryan Mccreery of Omaha NE (US)
Gabrielle Merchant of Omaha NE (US)
PREDICTING REAL-EAR-TO-COUPLER DIFFERENCES BASED ON CLINICAL IMMITTANCE MEASURES OF THE MIDDLE EAR - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240056749 titled 'PREDICTING REAL-EAR-TO-COUPLER DIFFERENCES BASED ON CLINICAL IMMITTANCE MEASURES OF THE MIDDLE EAR
Simplified Explanation
The abstract of the patent application describes a method for improving the accuracy of pediatric hearing-aid verification. Currently, the verification process relies on measures of output from the ear canal or in a coupler with the child's real-ear-to-coupler difference (RECD). However, these measures cannot always be completed in children, leading to inaccuracies in fitting when average RECD values are used instead.
To address this issue, the patent application proposes creating a machine-learned model that incorporates and refines the modeling by incorporating clinical tympanometric data into predictions of individual RECDs. This approach resulted in more accurate estimates and smaller errors compared to using age-based average RECD alone. The modeling can be included in clinical diagnostic tools to quickly and non-invasively provide improved estimation for pediatric hearing-aid verification in a clinical setting.
- The patent application proposes a machine-learned model to improve pediatric hearing-aid verification.
- The model incorporates clinical tympanometric data to predict individual real-ear-to-coupler differences (RECDs).
- This approach provides more accurate estimates and reduces errors compared to using average RECD values.
- The modeling can be integrated into clinical diagnostic tools for improved estimation in a non-invasive manner.
Potential applications of this technology:
- Pediatric hearing-aid fitting and verification in clinical settings.
- Improving the accuracy of hearing-aid adjustments for children.
Problems solved by this technology:
- Inaccuracies in pediatric hearing-aid fitting due to the inability to complete certain measures in children.
- Reliance on average RECD values, which may not accurately represent individual variations.
Benefits of this technology:
- More accurate estimation of individual RECDs for pediatric hearing-aid fitting.
- Improved customization and adjustment of hearing aids for children.
- Non-invasive and quick estimation using clinical diagnostic tools.
Original Abstract Submitted
pediatric hearing-aid verification relies on probe microphone measures of output from the ear canal or in a coupler with the child's real-ear-to-coupler difference (recd). these measures cannot always be completed in children, leading to inaccuracies in fitting when average recd values are used instead. audiologists often have tympanometry data that characterizes the impedance of outer and middle ear. creating a machine-learned model to train itself to incorporate and refine the modelling such as by incorporating clinical tympanometric data into predictions of individual recds led to more accurate estimates and smaller errors than using age-based average recd alone. the modelling can be included in clinical diagnostic tools to quickly and non-invasively provide improved estimation for pediatric hearing-aid verification in a clinical setting.
