# US 12,762,697 B2 · Method and system for calculating parameters in larynx image with artificial intelligence assistance

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- Number: US 12,762,697 B2
- Document ID: US12762697B2
- Status: Granted
- Kind: B2
- Application number: 18/512,018
- Filed: 2023-11-17
- Published: 2024-05-23
- Granted: 2026-10-13
- Earliest claimed: 2022-11-17
- Assignees: Changhua Christian Medical Foundation Changhua Christian Hospital
- Applicants: Changhua Christian Medical Foundation Changhua Christian Hospital
- Inventors: Andy Chen; Acquah Hackman; Mu-Kuan Chen; Chih-Chin Liu
- Web page: https://patentgrep.com/patent/US12762697B2

## CPC

- G06T 7/0012: {Biomedical image inspection}
- A61B 1/267: for the respiratory tract, e.g. laryngoscopes or bronchoscopes
- G06T 7/11: Region-based segmentation
- G06T 7/13: Edge detection
- G06T 9/00: Image coding (bandwidth or redundancy reduction for static pictures H04N1/41; coding or decoding of static colour picture signals H04N1/64; methods or arrangements for coding, decoding, compressing or decompressing digital video signals H04N19/00)
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)

## Abstract

A method for calculating parameters in a larynx image with an artificial intelligence assistance includes training a deep learning object detection software and a deep learning image recognition and segmentation software to extract a glottis image from a larynx image and recognize a membranous glottal gap; after receiving a larynx image, a plurality of larynx images captured frame-by-frame, or a larynx video that is captured when vocal folds are in a phonating state, extracting a glottis image by the deep learning object detection software; recognizing a membranous glottal gap in the glottis image and correspondingly outputting a membranous glottal gap filter by the deep learning image recognition and segmentation software; performing image processing of edge detection and image patching on the membranous glottal gap filter to clearly outline the membranous glottal gap and obtaining a medical parameter of several vocal fold anatomies from the clearly outlined membranous glottal gap.

## Claims

### Claim 1

A method for calculating parameters in a larynx image with an artificial intelligence assistance, comprising:
training a deep learning object detection software by a plurality of larynx images with a manually marked glottis image region to extract a glottis image from a larynx image received and training a deep learning image recognition and segmentation software by a plurality of glottis images with a manually marked membranous glottal gap to recognize a membranous glottal gap in a glottis image received; the membranous glottal gap includes structural features of a left vocal process, a right vocal process, and an anterior commissure;
receiving a larynx image, a plurality of larynx images captured frame-by-frame, or a larynx video that is captured when vocal folds are in a phonating state;
extracting at least one glottis image from the larynx image, the plurality of larynx images, or the larynx video by the deep learning object detection software;
recognizing a membranous glottal gap in the at least one glottis image by the deep learning image recognition and segmentation software and outputting at least one membranous glottal gap filter corresponding to the at least one glottis image by the deep learning image recognition and segmentation software; and
performing image processing of edge detection and image inpainting on the at least one membranous glottal gap filter to clearly outline a membranous glottal gap in the at least one membranous glottal gap filter and obtaining a medical parameter of a plurality of vocal fold anatomies from the clearly outlined membranous glottal gap in the at least one membranous glottal gap filter;
wherein during recognizing the membranous glottal gap in the at least one glottis image, the at least one glottis image is compressed to the at least one glottis image in small size at first; then the deep learning image recognition and segmentation software recognizes the membranous glottal gap in the at least one glottis image in small size and outputs the at least one membranous glottal gap filter in small size corresponding to the at least one glottis image in small size; the at least one membranous glottal gap filter in small size is restored to the at least one membranous glottal gap filter in original size to be output for subsequently performing image processing of edge detection and image inpainting.

### Claim 2 (depends on 1)

The method as claimed in claim 1, wherein during obtaining the medical parameter, the medical parameter obtained is a normalized membranous glottal gap area; a membranous glottal gap area of the at least one glottis image is calculated from the clearly outlined membranous glottal gap; a vocal fold length is obtained from the same clearly outlined membranous glottal gap; the vocal fold length is a straight-line distance from the left vocal process to the anterior commissure or from the right vocal process to the anterior commissure; the normalized membranous glottal gap area is calculated from the membranous glottal gap area and the vocal fold length.

### Claim 3 (depends on 1)

The method as claimed in claim 1, wherein during obtaining the medical parameter, the medical parameter obtained is an amplitude of vocal fold vibration; the amplitude of vocal fold vibration is a longest distance from a linkage of the left vocal process or the right vocal process and the anterior commissure during vocal fold adduction to a side edge of the membranous glottal gap in a direction perpendicular to the linkage.

### Claim 4 (depends on 2)

The method as claimed in claim 2, wherein after obtaining the medical parameter, the method further comprises preparing a graph with a horizontal axis of a capturing time or a frame order and a vertical axis of the normalized membranous glottal gap area.