AIMC Topic: Endophthalmitis

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Machine Learning Approach for Intraocular Disease Prediction Based on Aqueous Humor Immune Mediator Profiles.

Ophthalmology Jan 21, 2021
PURPOSE: Various immune mediators have crucial roles in the pathogenesis of intraocular diseases. Machine learning can be used to automatically select and weigh various predictors to develop models maximizing predictive power. However, these techniqu...
Aged Aged, 80 and over Reproducibility of Results Male Middle Aged Cross-Sectional Studies Adult Humans Female Machine Learning Immunoassay Endophthalmitis Area Under Curve Glaucoma, Open-Angle Intraocular Lymphoma Inflammation Mediators Eye Diseases ROC Curve Retinal Necrosis Syndrome, Acute Flow Cytometry Interleukins Diagnosis, Computer-Assisted Aqueous Humor Retinal Detachment
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Text Processing for Detection of Fungal Ocular Involvement in Critical Care Patients: Cross-Sectional Study.

Journal of medical Internet research Aug 14, 2020
BACKGROUND: Fungal ocular involvement can develop in patients with fungal bloodstream infections and can be vision-threatening. Ocular involvement has become less common in the current era of improved antifungal therapies. Retrospectively determining...
Eye Endophthalmitis Critical Care Retrospective Studies Humans Middle Aged Male Risk Factors Mycoses Natural Language Processing Cross-Sectional Studies Female
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Gradient Boosted Decision Tree Classification of Endophthalmitis Versus Uveitis and Lymphoma from Aqueous and Vitreous IL-6 and IL-10 Levels.

Journal of ocular pharmacology and therapeutics : the official journal of the Association for Ocular Pharmacology and Therapeutics Feb 3, 2017
PURPOSE: To investigate the effectiveness of gradient boosting to classify endophthalmitis versus uveitis and lymphoma by intraocular cytokine levels.
Endophthalmitis Uveitis Decision Trees Data Interpretation, Statistical Lymphoma Humans Water Machine Learning ROC Curve Interleukin-6 Interleukin-10
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Evaluation of the reliability and readability of answers given by chatbots to frequently asked questions about endophthalmitis: A cross-sectional study on chatbots.

Health informatics journal Jan 1, 2024
This study aimed to investigate the accuracy, reliability, and readability of A-Eye Consult, ChatGPT-4.0, Google Gemini and Copilot AI large language models (LLMs) in responding to patient questions about endophthalmitis. The LLMs' responses to 25 ...
Endophthalmitis Surveys and Questionnaires Comprehension Reproducibility of Results Internet Cross-Sectional Studies Humans
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