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Cataract Extraction

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Exploring Artificial Intelligence Programs' Understanding of Lens, Cataract, and Refractive Surgery Information.

Middle East African journal of ophthalmology
PURPOSE: We aimed to evaluate the success of Chat Generative Pre-trained Transformer (ChatGPT), Bing, and Bard artificial intelligence programs, which were released free of charge by three different manufacturers, in correctly answering questions abo...

PhacoTrainer: Automatic Artificial Intelligence-Generated Performance Ratings for Cataract Surgery.

Translational vision science & technology
PURPOSE: To investigate whether cataract surgical skill performance metrics automatically generated by artificial intelligence (AI) models can differentiate between trainee and faculty surgeons and the correlation between AI metrics and expert-rated ...

Hotspots and trends of artificial intelligence in the field of cataracts: a bibliometric analysis.

International ophthalmology
PURPOSE: To analyze the hotspots and trends in artificial intelligence (AI) research in the field of cataracts.

Artificial intelligence applications in cataract and refractive surgeries.

Current opinion in ophthalmology
PURPOSE OF REVIEW: This review highlights the recent advancements in the applications of artificial intelligence within the field of cataract and refractive surgeries. Given the rapid evolution of artificial intelligence technologies, it is essential...

Enhancing online cataract surgery patient education materials through artificial intelligence.

Canadian journal of ophthalmology. Journal canadien d'ophtalmologie
OBJECTIVE: To assess the feasibility of using artificial intelligence (AI) to improve readability of online cataract surgery patient education materials (PEMs) in English and Spanish.

Beyond PhacoTrainer: Deep Learning for Enhanced Trabecular Meshwork Detection in MIGS Videos.

Translational vision science & technology
PURPOSE: The purpose of this study was to develop deep learning models for surgical video analysis, capable of identifying minimally invasive glaucoma surgery (MIGS) and locating the trabecular meshwork (TM).

Evaluation of prediction errors in nine intraocular lens calculation formulas using an explainable machine learning model.

BMC ophthalmology
BACKGROUND: The purpose of the study was to evaluate the relationship between prediction errors (PEs) and ocular biometric variables in cataract surgery using nine intraocular lens (IOL) formulas with an explainable machine learning model.

Prediction of Visual Acuity After Cataract Surgery by Deep Learning Methods Using Clinical Information and Color Fundus Photography.

Current eye research
PURPOSE: To examine the performance of deep-learning models that predicts the visual acuity after cataract surgery using preoperative clinical information and color fundus photography (CFP).

Factors influencing the estimation of phacoemulsification procedure time in cataract surgery: Analysis using neural networks.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Realistic and accurate estimation of the surgery duration is one of the key factors influencing the optimization of hospital work and, consequently, the planning and management of the budget. In the present study, the author...