Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Imaging Studies VII: Vascular Imaging

Imaging Studies VII: Vascular Imaging

DefinitionRenal angiography, also known as renal arteriography, is an imaging technique used to obtain a comprehensive view of blood flow and the vascular structure of blood vessels in the kidneys and surrounding areas.PurposeRenal angiography detects blood vessel abnormalities in the kidneys, such as aneurysms, stenosis, thrombosis, vascular tumors, and renal artery stenosis. It evaluates kidney function and guides interventional treatments like angioplasty or stent placement.Pre-Procedure...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Vision-related quality of life in non-neovascular age-related macular degeneration: a protocol for systematic review and meta-analysis.

Eye (London, England)·2026

Same author

Surgical Management and Outcomes of Large High Myopic Macular Holes: Global Macular Hole Multicenter Study 3.

Retina (Philadelphia, Pa.)·2026

Same author

OphthoEvidence: a model for rapid and trustworthy ophthalmology guidelines.

Eye (London, England)·2026

Same author

Comparative flow and operating efficiency of TDC Veloce 27-gauge vitrectome.

Retina (Philadelphia, Pa.)·2026

Same author

Rhegmatogenous Retinal Detachment Associated with Giant Retinal Tears: The Effect of Tamponade Choice on Outcome.

Ophthalmologica. Journal international d'ophtalmologie. International journal of ophthalmology. Zeitschrift fur Augenheilkunde·2026

Same author

Vitreous collagen fragment dimensions after vitrectomy: a pilot study addressing the cut-rate enigma.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie·2026

Same journal

Prognostic Value of Interocular Flicker ERG Asymmetry in Acute Postoperative Endophthalmitis After Intraocular Surgery: A Pilot Study.

Journal of ophthalmology·2026

Same journal

Clinical Ophthalmology Consultations in Paediatric Inpatient Rehabilitation-A Retrospective Case-Note Analysis.

Journal of ophthalmology·2026

Same journal

Pathogenesis of Neovascular Glaucoma in Diabetic Retinopathy: A Review.

Journal of ophthalmology·2026

Same journal

Mathematical Modeling of IOL Power Changes After Pupil Dilation Based on Multifactorial Analysis.

Journal of ophthalmology·2026

Same journal

Reduced Neutrophil-to-Eosinophil Ratio in Chronic Dacryocystitis: A Hematological Clue to Allergic Pathogenesis.

Journal of ophthalmology·2026

Same journal

Comparative Topographic and Tomographic Assessment of Post-LASIK Corneal Ectasia.

Journal of ophthalmology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 17, 2025

Author Spotlight: Standardizing Mouse In Vivo PET Imaging with Body Conforming Molds and Automated Analysis

Author Spotlight: Standardizing Mouse In Vivo PET Imaging with Body Conforming Molds and Automated Analysis

Published on: October 25, 2024

Biomedical Data Annotation: An OCT Imaging Case Study.

Matthew Anderson¹, Salman Sadiq², Muzammil Nahaboo Solim²

¹School of Computing, Newcastle University, Urban Sciences Building, Newcastle upon Tyne NE4 5TG, UK.

Journal of Ophthalmology

|August 31, 2023

Summary

This summary is machine-generated.

Annotation quality for diabetic macular edema (DME) on optical coherence tomography (OCT) scans varies with clinician experience. Multiple annotation sessions showed limited improvement in intraretinal fluid (IRF) biomarker accuracy.

More Related Videos

Automated 3D Optical Coherence Tomography to Elucidate Biofilm Morphogenesis Over Large Spatial Scales

Automated 3D Optical Coherence Tomography to Elucidate Biofilm Morphogenesis Over Large Spatial Scales

Published on: August 21, 2019

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Related Experiment Videos

Last Updated: Jul 17, 2025

Author Spotlight: Standardizing Mouse In Vivo PET Imaging with Body Conforming Molds and Automated Analysis

Author Spotlight: Standardizing Mouse In Vivo PET Imaging with Body Conforming Molds and Automated Analysis

Published on: October 25, 2024

Automated 3D Optical Coherence Tomography to Elucidate Biofilm Morphogenesis Over Large Spatial Scales

Automated 3D Optical Coherence Tomography to Elucidate Biofilm Morphogenesis Over Large Spatial Scales

Published on: August 21, 2019

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Optical coherence tomography (OCT) is crucial for eye structure visualization and disease detection.
Deep learning models require extensive, high-quality annotated datasets for accurate OCT image analysis.
Clinical annotation bottlenecks necessitate exploring annotations from clinicians with varying experience levels.

Purpose of the Study:

To evaluate the quality of intraretinal fluid (IRF) biomarker annotations in diabetic macular edema (DME) on OCT B-scans.
To assess the impact of clinician experience on annotation quality.
To determine the effectiveness of multiple annotation sessions after expert training.

Main Methods:

Five clinicians with diverse experience levels annotated IRF biomarkers on OCT B-scans of DME patients.
Annotation quality was assessed based on accuracy and consistency.
The effect of multiple annotation sessions following expert-led training was evaluated.

Main Results:

Significant variance in annotation performance was observed among clinicians.
Annotation quality correlated positively with the clinician's experience in interpreting OCT images of DME.
Multiple annotation sessions demonstrated a limited positive impact on overall annotation quality.

Conclusions:

Clinician experience is a critical factor influencing the reliability of OCT image annotations for DME.
Relying on less experienced annotators may compromise data quality for deep learning models.
Standardized training and quality control measures are essential for improving annotation consistency in clinical AI development.