AIMC Topic: Visual Acuity
Clear Filters Showing 11 to 20 of 166 articles
Use of a Convolutional Neural Network to Predict the Response of Diabetic Macular Edema to Intravitreal Anti-VEGF Treatment: A Pilot Study.
American journal of ophthalmology
Feb 20, 2025
PURPOSE: To utilize a convolutional neural network (CNN) to predict the response of treatment-naïve diabetic macular edema (DME) to a single injection of anti-vascular endothelial growth factor (anti-VEGF) with data from optical coherence tomography ...
Humans
Diabetic Retinopathy
Neural Networks, Computer
Retrospective Studies
Pilot Projects
Angiogenesis Inhibitors
Treatment Outcome
Follow-Up Studies
Visual Acuity
Tomography, Optical Coherence
Female
Aged
Male
Middle Aged
Convolutional Neural Networks
Macular Edema
Vascular Endothelial Growth Factor A
Intravitreal Injections
Ranibizumab
Bevacizumab
Classification of fundus autofluorescence images based on macular function in retinitis pigmentosa using convolutional neural networks.
Japanese journal of ophthalmology
Feb 12, 2025
PURPOSE: To determine whether convolutional neural networks (CNN) can classify the severity of central vision loss using fundus autofluorescence (FAF) images and color fundus images of retinitis pigmentosa (RP), and to evaluate the utility of those i...
Using a Deep Learning Model to Predict Postoperative Visual Outcomes of Idiopathic Epiretinal Membrane Surgery.
American journal of ophthalmology
Jan 13, 2025
PURPOSE: This study assessed the performance of various deep learning models in predicting the postoperative outcomes of idiopathic epiretinal membrane (ERM) surgery based on preoperative optical coherence tomography (OCT) images.
Estimating Visual Acuity With Spectacle Correction From Fundus Photos Using Artificial Intelligence.
JAMA network open
Jan 2, 2025
IMPORTANCE: Determining spectacle-corrected visual acuity (VA) is essential when managing many ophthalmic diseases. If artificial intelligence (AI) evaluations of macular images estimated this VA from a fundus image, AI might provide spectacle-correc...
Analyzing the Effect of Surgical and Corneal Parameters on the Postoperative Refractive Outcomes of Smile in Myopic Eyes Based on Machine Learning.
American journal of ophthalmology
Dec 27, 2024
PURPOSE: To analyze the influence of individual parameters on the postoperative refractive outcomes of small incision lenticule extraction (SMILE) in myopic eyes using machine learning.
Predicting macular hole surgery outcomes: Integrating preoperative OCT features with supervised machine learning statistical models.
Indian journal of ophthalmology
Dec 24, 2024
PURPOSE: To evaluate various supervised machine learning (ML) statistical models to predict anatomical outcomes after macular hole (MH) surgery using preoperative optical coherence tomography (OCT) features.
Evaluation of prediction errors in nine intraocular lens calculation formulas using an explainable machine learning model.
BMC ophthalmology
Dec 19, 2024
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.
Artificial intelligence-assisted fitting method using corneal topography outcomes enhances success rate in orthokeratology lens fitting.
Contact lens & anterior eye : the journal of the British Contact Lens Association
Dec 16, 2024
PURPOSE: Based on ideal outcomes of corneal topography following orthokeratology (OK), an innovative machine learning algorithm for corneal refractive therapy (CRT) was developed to investigate the precision of artificial intelligence (AI)-assisted O...
A clinical practical model for preoperative prediction of visual outcome for pituitary adenoma patients in a retrospective and prospective study.
Frontiers in endocrinology
Dec 13, 2024
OBJECTIVE: Preoperative prediction of visual recovery after pituitary adenoma resection surgery remains challenging. This study aimed to investigate the value of clinical and radiological features in preoperatively predicting visual outcomes after su...
Retinal fluid quantification using a novel deep learning algorithm in patients treated with faricimab in the TRUCKEE study.
Eye (London, England)
Dec 11, 2024
BACKGROUND: Investigate retinal fluid changes via a novel deep-learning algorithm in real-world patients receiving faricimab for the treatment of neovascular age-related macular degeneration (nAMD).
Stay Ahead of Medical AI
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.
Subscribe NowPopular Topics
- Machine Learning 30978
- Neural Networks, Computer 29696
- Algorithms 26813
- Deep Learning 26324
- Artificial Intelligence 24267
- Robotics 13307
- Image Processing, Computer-Assisted 9512
- Retrospective Studies 9097
- Magnetic Resonance Imaging 6173
- Reproducibility of Results 5589
- Tomography, X-Ray Computed 4765
- Support Vector Machine 4678
- Computational Biology 4229
- Brain 4045
- Computer Simulation 3877
Recent Journals
- Scientific reports 5865
- Sensors (Basel, Switzerland) 4794
- PloS one 4011
- Neural networks : the official journal of the International Neural Network Society 2928
- Computational intelligence and neuroscience 1931
- Computers in biology and medicine 1839
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 1686
- Studies in health technology and informatics 1278
- IEEE journal of biomedical and health informatics 1129
- Journal of chemical information and modeling 994
- Nature communications 938
- Computer methods and programs in biomedicine 883
- Bioinformatics (Oxford, England) 861
- Briefings in bioinformatics 850
- IEEE transactions on neural networks and learning systems 823