AIMC Topic: Cataract

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Deep learning-based correction of cataract-induced influence on macular pigment optical density measurement by autofluorescence spectroscopy.

PloS one
PURPOSE: Measurements of macular pigment optical density (MPOD) using the autofluorescence spectroscopy yield underestimations of actual values in eyes with cataracts. Previously, we proposed a correction method for this error using deep learning (DL...

Using Natural Language Processing to Identify Different Lens Pathology in Electronic Health Records.

American journal of ophthalmology
PURPOSE: Nearly all published ophthalmology-related Big Data studies rely exclusively on International Classification of Diseases (ICD) billing codes to identify patients with particular ocular conditions. However, inaccurate or nonspecific codes may...

Feasibility of an artificial intelligence phone call for postoperative care following cataract surgery in a diverse population: two phase prospective study protocol.

BMJ open ophthalmology
INTRODUCTION: Artificial intelligence (AI) development has led to improvements in many areas of medicine. Canada has workforce pressures in delivering cataract care. A potential solution is using AI technology that can automate care delivery, increas...

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...

The effect of optical degradation from cataract using a new Deep Learning optical coherence tomography segmentation algorithm.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
PURPOSE: To assess the validity of the results of a freely available online Deep Learning segmentation tool and its sensitivity to noise introduced by cataract.

Customization of a passive surgical support robot to specifications for ophthalmic surgery and preliminary evaluation.

Japanese journal of ophthalmology
PURPOSE: To customize a passive surgery support robot for ophthalmic surgery and preliminarily evaluate its performance.

Development and validation of a pixel wise deep learning model to detect cataract on swept-source optical coherence tomography images.

Journal of optometry
PURPOSE: The diagnosis of cataract is mostly clinical and there is a lack of objective and specific tool to detect and grade it automatically. The goal of this study was to develop and validate a deep learning model to detect and localize cataract on...

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.

Detecting visually significant cataract using retinal photograph-based deep learning.

Nature aging
Age-related cataracts are the leading cause of visual impairment among older adults. Many significant cases remain undiagnosed or neglected in communities, due to limited availability or accessibility to cataract screening. In the present study, we r...