AIMC Topic: Tomography, Optical Coherence

Clear Filters Showing 641 to 650 of 857 articles

Deep Learning Differentiates Papilledema, NAION, and Healthy Eyes With Unsegmented 3D OCT Volumes.

American journal of ophthalmology
OBJECTIVE: Deep learning (DL) has been used to differentiate papilledema from healthy eyes and optic disc elevation on fundus photos. As we described optic nerve head (ONH) and peripapillary retina (PPR) optical coherence tomography (OCT) features th...

Translating the machine; An assessment of clinician understanding of ophthalmological artificial intelligence outputs.

International journal of medical informatics
INTRODUCTION: Advances in artificial intelligence offer the promise of automated analysis of optical coherence tomography (OCT) scans to detect ocular complications from anticancer drug therapy. To explore how such AI outputs are interpreted in clini...

Diagnostic report generation for macular diseases by natural language processing algorithms.

The British journal of ophthalmology
AIMS: To investigate rule-based and deep learning (DL)-based methods for the automatically generating natural language diagnostic reports for macular diseases.

Optical coherence tomography angiography as a tool for diagnosis and monitoring of sickle cell related eye disease: a systematic review and meta-analysis.

Eye (London, England)
Sickle cell retinopathy (SCR) is an ocular manifestation of sickle cell disease (SCD). In SCR abnormal sickling of erythrocytes is associated with sight-threatening complications such as neovascularisation, vitreous haemorrhage, maculopathy and retin...

Deep learning generalization study on optical coherence tomography image denoising.

Physics in medicine and biology
Noise is a key factor determining imaging quality for optical coherence tomography (OCT). Although deep learning has emerged as an effective denoising method, its generalization capability remains limited, especially when test noise levels deviate fr...

Fundus Refraction Offset as a Personalized Biomarker for 12-Year Risk of Retinal Detachment.

Investigative ophthalmology & visual science
PURPOSE: The purpose of this study was to investigate the potential of a novel anatomical metric of ametropia-fundus refraction offset (FRO)-in stratifying the risk of retinal detachment (RD) or breaks, beyond the influence of risk factors including ...

Equitable Deep Learning for Diabetic Retinopathy Detection Using Multidimensional Retinal Imaging With Fair Adaptive Scaling.

Translational vision science & technology
PURPOSE: To investigate the fairness of existing deep models for diabetic retinopathy (DR) detection and introduce an equitable model to reduce group performance disparities.

Towards Investigating Residual Hearing Loss: Quantification of Fibrosis in a Novel Cochlear OCT Dataset.

IEEE transactions on bio-medical engineering
OBJECTIVE: Cochlear implants (CIs) are bionic prostheses that restores hearing via electrical stimulation of the auditory nerve. Hybrid CIs, which use electroacoustic stimulation (EAS), combine residual low-frequency acoustic hearing with CI electric...

Comparative Analysis of Automated vs. Expert-Designed Machine Learning Models in Age-Related Macular Degeneration Detection and Classification.

Turkish journal of ophthalmology
OBJECTIVES: To compare the effectiveness of expert-designed machine learning models and code-free automated machine learning (AutoML) models in classifying optical coherence tomography (OCT) images for detecting age-related macular degeneration (AMD)...

OCT in dermatology: a process for determining whether a fully diversified dataset is needed for AI model-building.

Optics letters
Optical coherence tomography (OCT) has sufficient depth penetration for detection of skin pathologies, but its detection effectiveness can be aided by the assistance of artificial intelligence (AI) modeling. AI model-building identifies pathologies b...