New algorithm for corneal densitometry assessment based on anterior segment optical coherence tomography.
Eye (Lond)
; 36(8): 1675-1680, 2022 08.
Article
in En
| MEDLINE
| ID: mdl-34341484
ABSTRACT
PURPOSE:
To describe a new algorithm to measure corneal densitometry based on images obtained by swept source anterior segment ocular coherence tomography (SS-AS-OCT) and establish standard densitometry values in a group of normal eyes.METHODS:
A total of 111 healthy participants (195 eyes) were enrolled in this study. Using a MATLAB designed algorithm, the cornea was segmented into three layers anterior, posterior and mid-stroma, and it was divided into two concentric areas, 0-2 and 2-4 mm, resulting in nine areas for the analysis. The mean corneal densitometry values were calculated and expressed as grayscale units (GSU).RESULTS:
The mean age was 57 years (range 22-87), with 100 (51.3%) right eyes and 95 (48.7%) left eyes. The total corneal densitometry was 86.9 ± 12.1 GSU. The mid-stroma layer had the highest densitometry values, 87.4 ± 12.1 GSU, and the anterior layer had the lowest values, 81.9 ± 14.2 GSU. Densitometry differences between the anterior layer and the mid-stroma layer (P < 0.001), as well as the anterior layer and the posterior layer (P < 0.05) were statistically significant. The 0-2 mm concentric area had higher mean densitometry values, 97.8 ± 12.7 GSU, and the differences were significant compared to the 2-4 mm concentric area (P < 0.001). No correlation was found between the corneal densitometry values and gender or age.CONCLUSIONS:
The new MATLAB segmentation algorithm for the analysis of corneal SS-AS-OCT images is capable to objectively assess corneal densitometry. We provide standard and normal data for better clinical and research approach.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Cornea
/
Tomography, Optical Coherence
Type of study:
Prognostic_studies
Limits:
Adult
/
Aged
/
Aged80
/
Humans
/
Middle aged
Language:
En
Journal:
Eye (Lond)
Journal subject:
OFTALMOLOGIA
Year:
2022
Document type:
Article
Affiliation country:
Spain