Latest AI and machine learning research in ophthalmology for healthcare professionals.
BACKGROUND: Stereoelectroencephalography (SEEG) is an effective technique to help to locate and to delimit the epileptogenic area and/or to define relationships with functional cortical areas. We intend to describe the surgical technique and verify the accuracy, safety, and effectiveness of robot-assisted SEEG in a newly created SEEG program in a pediatric center. We focus on the technical difficu...
BACKGROUND: Electronic medical records provide large-scale real-world clinical data for use in developing clinical decision systems. However, sophisticated methodology and analytical skills are required to handle the large-scale datasets necessary for the optimisation of prediction accuracy. Myopia is a common cause of vision loss. Current approaches to control myopia progression are effective but...
Epistasis learning, which is aimed at detecting associations between multiple Single Nucleotide Polymorphisms (SNPs) and complex diseases, has gained ...
Presence of exudates on a retina is an early sign of diabetic retinopathy, and automatic detection of these can improve the diagnosis of the disease. ...
Disease diagnosis from medical images has become increasingly important in medical science. Abnormality identification in retinal images has become a ...
Computer aided diagnosis (CAD) tools help radiologists to reduce diagnostic errors such as missing tumors and misdiagnosis. Vision researchers have be...
Artificial intelligence (AI) based on deep learning (DL) has sparked tremendous global interest in recent years. DL has been widely adopted in image r...
The identification and quantification of markers in medical images is critical for diagnosis, prognosis, and disease management. Supervised machine le...
Recently, researchers have built new deep learning (DL) models using a single image modality to diagnose age-related macular degeneration (AMD). Retin...
The ability to accurately forecast seizures could significantly improve the quality of life of patients with drug-refractory epilepsy. Prediction capa...
PURPOSE: To determine whether a machine learning technique called Kalman filtering (KF) can accurately forecast future values of mean deviation (MD), ...
PURPOSE: We sought to construct and evaluate a deep learning (DL) model to diagnose early glaucoma from spectral-domain optical coherence tomography (...
Deep learning algorithms produces state-of-the-art results for different machine learning and computer vision tasks. To perform well on a given task, ...
Maculopathy is the group of diseases that affects central vision of a person and they are often associated with diabetes. Many researchers reported au...
The Purpose of the study was to develop a deep residual learning algorithm to screen for glaucoma from fundus photography and measure its diagnostic p...
BACKGROUND: Robotized transcranial magnetic stimulation (TMS) combines the benefits of neuro-navigation with automation and provides a precision brain...
Artificial intelligence (AI) has emerged as a major frontier in computer science research. Although AI has broad application across many medical field...
Automatic retinal vessel segmentation is a fundamental step in the diagnosis of eye-related diseases, in which both thick vessels and thin vessels are...
Artificial intelligence (AI) is a branch of computer science that deals with the development of algorithms that seek to simulate human intelligence. W...
Superresolution localization microscopy strongly relies on robust identification algorithms for accurate reconstruction of the biological systems it i...