Publication Date
1-1-2024
Journal
Frontiers in Medicine
DOI
10.3389/fmed.2024.1351013
PMID
39026551
PMCID
PMC11254625
PubMedCentral® Posted Date
7-4-2024
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes
Keywords
dry eye, tear stability, tear break-up time, interferometry, artificial intelligence
Abstract
PURPOSE: The purpose of this study is to characterize and discuss the difference between software-detected non-invasive tear break-up time (NIBUT) and the traditional clinical method of fluorescein break-up time (FBUT).
METHODS: Tear interferometry with the KOWA DR-1α (Kowa, Japan) and a standardized comprehensive ocular surface/tear evaluation were performed in 307 eyes. Software-detected NIBUT in the KOWA DR-1α images and the investigator-detected FBUT were compared.
RESULTS: Software-detected NIBUT was significantly shorter than investigator-measured FBUT. NIBUT was 3.1 ± 2.5 s (mean ± SD), whereas FBUT was 4.8 ± 3.0 s. This difference was due to three different patterns or conditions: a spot break immediately after eyelid opening, moderate to severe keratitis sicca, and epithelial basement membrane corneal dystrophy (EBMD). In these cases, rapid tear film disruption was not captured by FBUT. A spot break immediately after eye opening that rapidly disappears was observed with conjunctivochalasis. This type of break-up may be difficult to detect using fluorescein because the human eye cannot catch such rapid blinks or post-blink events. In the second group with severe corneal epithelial disease, break-up may occur over the entire corneal surface upon eye opening, and distinct fluorescein tear break-up may not be identified because of poor dye dilution or spread over the corneal surface, whereas the non-invasive break-up is not solution-dependent, and the software can detect a distinct appearance. In the third group with EBMD, it is possible that focal break-up in the fluorescein pattern over the epithelial elevations, which might be missed visually, can be detected by software in video images.
CONCLUSION: We found that software-detected NIBUT is more sensitive in detecting tear break-up, can identify certain tear film disruptions that are missed by traditional FBUT, and may be more useful in distinguishing certain tear disorders.