Ophthalmology

A Deep Learning Method for Automatic Identification of Drusen and Macular Hole from Optical Coherence Tomography.

Deep Learning methods have become dominant in various fields of medical imaging, including ophthalmology. In this preliminary study, we investigated a method based on Convolutional Neural Network for the identification of drusen and macular hole from Optical Coherence Tomography scans with the aim to assist ophthalmologists in diagnosing and assessing retinal diseases.

Contrast sensitivity functions in autoencoders.

Three decades ago, Atick et al. suggested that human frequency sensitivity may emerge from the enhancement required for a more efficient analysis of retinal images. Here we reassess the relevance of low-level vision tasks in the explanation of the contrast sensitivity functions (CSFs) in light of 1) the current trend of using artificial neural networks for studying vision, and 2) the current knowl...

Deep Learning for Glaucoma Detection and Identification of Novel Diagnostic Areas in Diverse Real-World Datasets.

PURPOSE: To develop a three-dimensional (3D) deep learning algorithm to detect glaucoma using spectral-domain optical coherence tomography (SD-OCT) op...

Lens Opacities Classification System III-based artificial intelligence program for automatic cataract grading.

PURPOSE: To establish and validate an artificial intelligence (AI)-assisted automatic cataract grading program based on the Lens Opacities Classificat...

SYSTEMATIC CORRELATION OF CENTRAL SUBFIELD THICKNESS WITH RETINAL FLUID VOLUMES QUANTIFIED BY DEEP LEARNING IN THE MAJOR EXUDATIVE MACULAR DISEASES.

PURPOSE: To investigate the correlation of volumetric measurements of intraretinal (IRF) and subretinal fluid obtained by deep learning and central re...

Embedded AI system for interactive vision screen based on human action recognition.

In recent years, vision screening has emerged globally for employment (on a yearly basis) within primary and high schools since myopia heavily affects...

3D time-lapse imaging of a mouse embryo using intensity diffraction tomography embedded inside a deep learning framework.

We present a compact 3D diffractive microscope that can be inserted directly in a cell incubator for long-term observation of developing organisms. Ou...

Antegrade Colonic Enema Channels in Pediatric Patients Using Appendix or Cecal Flap: A Comparative Robotic Open Series.

We present perioperative outcomes of a single-center experience with robot-assisted antegrade colonic enema (ACE) channel creation for the treatment ...

Detecting Glaucoma in the Ocular Hypertension Study Using Deep Learning.

IMPORTANCE: Automated deep learning (DL) analyses of fundus photographs potentially can reduce the cost and improve the efficiency of reading center a...

Utilizing human intelligence in artificial intelligence for detecting glaucomatous fundus images using human-in-the-loop machine learning.

PURPOSE: For diagnosing glaucomatous damage, we have employed a novel convolutional neural network (CNN) from TrueColor confocal fundus images to conq...

Commentary: Artificial intelligence-based screening of retina.

Development and validation of an offline deep learning algorithm to detect vitreoretinal abnormalities on ocular ultrasound.

PURPOSE: We describe our offline deep learning algorithm (DLA) and validation of its diagnostic ability to identify vitreoretinal abnormalities (VRA) ...

Deep-learning-based 3D object salient detection via light-field integral imaging.

This Letter proposes an effective light-field 3D saliency object detection (SOD) method, which is inspired by the idea that the spatial and angular in...

Predicting Visual Improvement After Macular Hole Surgery: A Combined Model Using Deep Learning and Clinical Features.

PURPOSE: The purpose of this study was to assess the feasibility of deep learning (DL) methods to enhance the prediction of visual acuity (VA) improve...

Prediction of Subjective Refraction From Anterior Corneal Surface, Eye Lengths, and Age Using Machine Learning Algorithms.

PURPOSE: To develop a machine learning regression model of subjective refractive prescription from minimum ocular biometry and corneal topography feat...

Machine Learning Prediction of Non-Coding Variant Impact in Human Retinal cis-Regulatory Elements.

PURPOSE: Prior studies have demonstrated the significance of specific cis-regulatory variants in retinal disease; however, determining the functional ...

[Pose Testing Method for Medical Service Robots Based on Vision System].

Aiming at the medical service robot used in the indoor environment, this study proposes a method to test the pose accuracy of its working surface base...

Distinguishing mirror from glass: A "big data" approach to material perception.

Distinguishing mirror from glass is a challenging visual inference, because both materials derive their appearance from their surroundings, yet we rar...

Predicting Glaucoma Progression Requiring Surgery Using Clinical Free-Text Notes and Transfer Learning With Transformers.

PURPOSE: We evaluated the use of massive transformer-based language models to predict glaucoma progression requiring surgery using ophthalmology clini...

DEVELOPMENT AND VALIDATION OF AN EXPLAINABLE ARTIFICIAL INTELLIGENCE FRAMEWORK FOR MACULAR DISEASE DIAGNOSIS BASED ON OPTICAL COHERENCE TOMOGRAPHY IMAGES.

PURPOSE: To develop and validate an artificial intelligence framework for identifying multiple retinal lesions at image level and performing an explai...