AIMC Topic: Cataract Extraction

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Dr. Google vs. Dr. ChatGPT: Exploring the Use of Artificial Intelligence in Ophthalmology by Comparing the Accuracy, Safety, and Readability of Responses to Frequently Asked Patient Questions Regarding Cataracts and Cataract Surgery.

Seminars in ophthalmology
PURPOSE: Patients are using online search modalities to learn about their eye health. While Google remains the most popular search engine, the use of large language models (LLMs) like ChatGPT has increased. Cataract surgery is the most common surgica...

Talking technology: exploring chatbots as a tool for cataract patient education.

Clinical & experimental optometry
CLINICAL RELEVANCE: Worldwide, millions suffer from cataracts, which impair vision and quality of life. Cataract education improves outcomes, satisfaction, and treatment adherence. Lack of health literacy, language and cultural barriers, personal pre...

TRandAugment: temporal random augmentation strategy for surgical activity recognition from videos.

International journal of computer assisted radiology and surgery
PURPOSE: Automatic recognition of surgical activities from intraoperative surgical videos is crucial for developing intelligent support systems for computer-assisted interventions. Current state-of-the-art recognition methods are based on deep learni...

Intelligent cataract surgery supervision and evaluation via deep learning.

International journal of surgery (London, England)
PURPOSE: To assess the performance of a deep learning (DL) algorithm for evaluating and supervising cataract extraction using phacoemulsification with intraocular lens (IOL) implantation based on cataract surgery (CS) videos.

Development of a code-free machine learning model for the classification of cataract surgery phases.

Scientific reports
This study assessed the performance of automated machine learning (AutoML) in classifying cataract surgery phases from surgical videos. Two ophthalmology trainees without coding experience designed a deep learning model in Google Cloud AutoML Video C...

DeepLensNet: Deep Learning Automated Diagnosis and Quantitative Classification of Cataract Type and Severity.

Ophthalmology
PURPOSE: To develop deep learning models to perform automated diagnosis and quantitative classification of age-related cataract from anterior segment photographs.

Dehaze of Cataractous Retinal Images Using an Unpaired Generative Adversarial Network.

IEEE journal of biomedical and health informatics
Cataracts are the leading cause of visual impairment worldwide. Examination of the retina through cataracts using a fundus camera is challenging and error-prone due to degraded image quality. We sought to develop an algorithm to dehaze such images to...

Customised Selection of the Haptic Design in C-Loop Intraocular Lenses Based on Deep Learning.

Annals of biomedical engineering
In order to increase the probability of having a successful cataract post-surgery, the customisation of the haptic design of the intraocular lens (IOL) according to the characteristics of the patient is recommended. In this study, we present two pred...