Ophthalmology

Classification of pachychoroid disease on ultrawide-field indocyanine green angiography using auto-machine learning platform.

AIMS: Automatic identification of pachychoroid maybe used as an adjunctive method to confirm the condition and be of help in treatment for macular diseases. This study investigated the feasibility of classifying pachychoroid disease on ultra-widefield indocyanine green angiography (UWF ICGA) images using an automated machine-learning platform.

Prospective evaluation of an artificial intelligence-enabled algorithm for automated diabetic retinopathy screening of 30 000 patients.

BACKGROUND/AIMS: Human grading of digital images from diabetic retinopathy (DR) screening programmes represents a significant challenge, due to the increasing prevalence of diabetes. We evaluate the performance of an automated artificial intelligence (AI) algorithm to triage retinal images from the English Diabetic Eye Screening Programme (DESP) into test-positive/technical failure versus test-neg...

Visual interaction networks: A novel bio-inspired computational model for image classification.

Inspired by biological mechanisms and structures in neuroscience, many biologically inspired visual computational models have been presented to provid...

Deep learning model to predict visual field in central 10° from optical coherence tomography measurement in glaucoma.

BACKGROUND/AIM: To train and validate the prediction performance of the deep learning (DL) model to predict visual field (VF) in central 10° from spec...

Retinal Specialist versus Artificial Intelligence Detection of Retinal Fluid from OCT: Age-Related Eye Disease Study 2: 10-Year Follow-On Study.

PURPOSE: To evaluate the performance of retinal specialists in detecting retinal fluid presence in spectral domain OCT (SD-OCT) scans from eyes with a...

Effect of color information on the diagnostic performance of glaucoma in deep learning using few fundus images.

PURPOSE: The purpose of this study was to evaluate the accuracy of the convolutional neural network (CNN) model in glaucoma identification with three ...

Explaining the Rationale of Deep Learning Glaucoma Decisions with Adversarial Examples.

PURPOSE: To illustrate what is inside the so-called black box of deep learning models (DLMs) so that clinicians can have greater confidence in the con...

F-FDG PET-guided diffusion tractography reveals white matter abnormalities around the epileptic focus in medically refractory epilepsy: implications for epilepsy surgical evaluation.

BACKGROUND: Hybrid PET/MRI can non-invasively improve localization and delineation of the epileptic focus (EF) prior to surgical resection in medicall...

From Local to Global: A Graph Framework for Retinal Artery/Vein Classification.

Fundus photography has been widely used for inspecting eye disorders by ophthalmologists or computer algorithms. Biomarkers related to retinal vessels...

Prediction of response to anti-vascular endothelial growth factor treatment in diabetic macular oedema using an optical coherence tomography-based machine learning method.

PURPOSE: To predict the anti-vascular endothelial growth factor (VEGF) therapeutic response of diabetic macular oedema (DME) patients from optical coh...

Dense anatomical annotation of slit-lamp images improves the performance of deep learning for the diagnosis of ophthalmic disorders.

The development of artificial intelligence algorithms typically demands abundant high-quality data. In medicine, the datasets that are required to tra...

Artificial Intelligence and Big Data in Diabetes Care: A Position Statement of the Italian Association of Medical Diabetologists.

Since the last decade, most of our daily activities have become digital. Digital health takes into account the ever-increasing synergy between advance...

CoRNeA: A Pipeline to Decrypt the Inter-Protein Interfaces from Amino Acid Sequence Information.

Decrypting the interface residues of the protein complexes provides insight into the functions of the proteins and, hence, the overall cellular machin...

Age-related Macular Degeneration: Nutrition, Genes and Deep Learning-The LXXVI Edward Jackson Memorial Lecture.

PURPOSE: To evaluate the importance of nutritional supplements, dietary pattern, and genetic associations in age-related macular degeneration (AMD); a...

Artificial Intelligence-Enabled ECG: a Modern Lens on an Old Technology.

PURPOSE OF REVIEW: To (i) review the concept of artificial intelligence (AI); (ii) summarize recent developments in artificial intelligence-enabled el...

Deep Learning-Based Detection of Pigment Signs for Analysis and Diagnosis of Retinitis Pigmentosa.

Ophthalmological analysis plays a vital role in the diagnosis of various eye diseases, such as glaucoma, retinitis pigmentosa (RP), and diabetic and h...

Diabetic Retinopathy Screening with Automated Retinal Image Analysis in a Primary Care Setting Improves Adherence to Ophthalmic Care.

PURPOSE: Retinal screening examinations can prevent vision loss resulting from diabetes but are costly and highly underused. We hypothesized that arti...

Objective evaluation of allergic conjunctival disease (with a focus on the application of artificial intelligence technology).

We have summarized the past efforts and results of objective measurement methods for conjunctival hyperemia classification. Severity classification us...

A Neural Network Approach to Quantify Blood Flow from Retinal OCT Intensity Time-Series Measurements.

Many diseases of the eye are associated with alterations in the retinal vasculature that are possibly preceded by undetected changes in blood flow. In...

Artificial intelligence for anterior segment diseases: Emerging applications in ophthalmology.

With the advancement of computational power, refinement of learning algorithms and architectures, and availability of big data, artificial intelligenc...