AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Dry Eye Syndromes

Showing 1 to 10 of 29 articles

Clear Filters

Deep learning-based fully automated grading system for dry eye disease severity.

PloS one
There is an increasing need for an objective grading system to evaluate the severity of dry eye disease (DED). In this study, a fully automated deep learning-based system for the assessment of DED severity was developed. Corneal fluorescein staining ...

Machine learning-based prediction of tear osmolarity for contact lens practice.

Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists)
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...

Automated tear film break-up time measurement for dry eye diagnosis using deep learning.

Scientific reports
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...

Enhancing Meibography Image Analysis Through Artificial Intelligence-Driven Quantification and Standardization for Dry Eye Research.

Translational vision science & technology
PURPOSE: This study enhances Meibomian gland (MG) infrared image analysis in dry eye (DE) research through artificial intelligence (AI). It is comprised of two main stages: automated eyelid detection and tarsal plate segmentation to standardize meibo...

[Practical Application of Intelligent Vision Measurement System Based on Deep Learning].

Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation
To comprehensively assess the true visual function of clinical dry eye patients and the comprehensive impact of blinking characteristics on functional vision of the human eye, an intelligent vision measurement system has been designed and developed t...

Applications of Artificial Intelligence in Diagnosis of Dry Eye Disease: A Systematic Review and Meta-Analysis.

Cornea
PURPOSE: Clinical diagnosis of dry eye disease is based on a subjective Ocular Surface Disease Index questionnaire or various objective tests, however, these diagnostic methods have several limitations.

Deep-learning based analysis of in-vivo confocal microscopy images of the subbasal corneal nerve plexus' inferior whorl in patients with neuropathic corneal pain and dry eye disease.

The ocular surface
PURPOSE: To evaluate and compare subbasal corneal nerve parameters of the inferior whorl in patients with dry eye disease (DED), neuropathic corneal pain (NCP), and controls using a novel deep-learning-based algorithm to analyze in-vivo confocal micr...

[Advancements of artificial intelligence in dry eye].

[Zhonghua yan ke za zhi] Chinese journal of ophthalmology
With the continuous evolution of computer technology and the surging advent of the big data era, artificial intelligence (AI) has already manifested extremely broad application prospects. Medical AI, like a capable assistant, can help doctors make mo...

[Vigorously advancing the application of AI in the diagnosis and treatment of ocular surface and tear diseases].

[Zhonghua yan ke za zhi] Chinese journal of ophthalmology
Ocular surface and tear diseases are among the most common and significant ocular conditions affecting eye health. In recent years, research and clinical diagnosis and treatment of ocular surface and tear diseases have rapidly developed in China, but...

Artificial intelligence models utilize lifestyle factors to predict dry eye related outcomes.

Scientific reports
The purpose of this study is to examine and interpret machine learning models that predict dry eye (DE)-related clinical signs, subjective symptoms, and clinician diagnoses by heavily weighting lifestyle factors in the predictions. Machine learning m...