AIMC Topic: Tears
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Performance of a novel multimodal large language model in ınterpreting meibomian glands quantitatively and qualitatively.
International ophthalmology
May 28, 2025
PURPOSE: To evaluate the performance of a multimodal large language model (LLM), Claude 3.5 Sonnet, in interpreting meibography images for Meibomian gland dropout grading and morphological abnormality detection.
Rapid detection of drug abuse via tear analysis using surface enhanced Raman spectroscopy and machine learning.
Scientific reports
Jan 7, 2025
With the growing global challenge of drug abuse, there is an urgent need for rapid, accurate, and cost-effective drug detection methods. This study introduces an innovative approach to drug abuse screening by quickly detecting ephedrine (EPH) in tear...
Automated tear film break-up time measurement for dry eye diagnosis using deep learning.
Scientific reports
May 22, 2024
In the realm of ophthalmology, precise measurement of tear film break-up time (TBUT) plays a crucial role in diagnosing dry eye disease (DED). This study aims to introduce an automated approach utilizing artificial intelligence (AI) to mitigate subje...
Machine learning-based prediction of tear osmolarity for contact lens practice.
Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists)
Mar 25, 2024
PURPOSE: This study addressed the utilisation of machine learning techniques to estimate tear osmolarity, a clinically significant yet challenging parameter to measure accurately. Elevated tear osmolarity has been observed in contact lens wearers and...
Deep learning framework for automated goblet cell density analysis in in-vivo rabbit conjunctiva.
Scientific reports
Dec 21, 2023
Goblet cells (GCs) in the conjunctiva are specialized epithelial cells secreting mucins for the mucus layer of protective tear film and playing immune tolerance functions for ocular surface health. Because GC loss is observed in various ocular surfac...
Artificial intelligence to estimate the tear film breakup time and diagnose dry eye disease.
Scientific reports
Apr 10, 2023
The use of artificial intelligence (AI) in the diagnosis of dry eye disease (DED) remains limited due to the lack of standardized image formats and analysis models. To overcome these issues, we used the Smart Eye Camera (SEC), a video-recordable slit...
Current uses of artificial intelligence in the analysis of biofluid markers involved in corneal and ocular surface diseases: a systematic review.
Eye (London, England)
Nov 15, 2022
Corneal and ocular surface diseases (OSDs) carry significant psychosocial and economic burden worldwide. We set out to review the literature on the application of artificial intelligence (AI) and bioinformatics for analysis of biofluid biomarkers in ...
Automatic identification of meibomian gland dysfunction with meibography images using deep learning.
International ophthalmology
Sep 19, 2022
BACKGROUND: Artificial intelligence is developing rapidly, bringing increasing numbers of intelligent products into daily life. However, it has little progress in dry eye, which is a common disease and associated with meibomian gland dysfunction (MGD...
Automated quantification of meibomian gland dropout in infrared meibography using deep learning.
The ocular surface
Jun 24, 2022
PURPOSE: Develop a deep learning-based automated method to segment meibomian glands (MG) and eyelids, quantitatively analyze the MG area and MG ratio, estimate the meiboscore, and remove specular reflections from infrared images.
Automatic screening of tear meniscus from lacrimal duct obstructions using anterior segment optical coherence tomography images by deep learning.
Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
Feb 12, 2021
PURPOSE: We assessed the ability of deep learning (DL) models to distinguish between tear meniscus of lacrimal duct obstruction (LDO) patients and normal subjects using anterior segment optical coherence tomography (ASOCT) images.
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