Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Comprehensive Pharmacokinetics of the Marine-Derived PDE4 Inhibitor LY104 and Its Major Metabolite M1 in Rats: A Validated LC-MS/MS Method with Sex Comparison, Multiple-Dose, Protein Binding, Metabolic Stability, and Excretion Studies.

Marine drugs·2026
Same author

Correction: Diagnostic performance and generalizability of deep learning for multiple retinal diseases using bimodal imaging of fundus photography and optical coherence tomography.

Frontiers in cell and developmental biology·2026
Same author

Discovery of Marine-Inspired Guanidine-Based PDE4 Inhibitors for the Treatment of Chronic Obstructive Pulmonary Disease.

Marine drugs·2026
Same author

Diagnostic performance and generalizability of deep learning for multiple retinal diseases using bimodal imaging of fundus photography and optical coherence tomography.

Frontiers in cell and developmental biology·2025
Same author

Long-term outcomes of corneal intrastromal lenticule transplantation for necrotic scleral melting and glaucoma: A case report.

Medicine·2025
Same author

Assessment of synthetic post-therapeutic OCT images using the generative adversarial network in patients with macular edema secondary to retinal vein occlusion.

Frontiers in cell and developmental biology·2025
Same journal

Novel technique for treating extraocular muscle adherence after fracture repair.

The British journal of ophthalmology·2026
Same journal

Safe use of fluorinated gases in vitreoretinal surgery: learning from patient safety incidents with expert panel recommendations from the British and Eire Association of Vitreoretinal Surgeons (BEAVRS).

The British journal of ophthalmology·2026
Same journal

Fast progressors in Asian normal-tension glaucoma: 10 years and beyond in a longitudinal cohort.

The British journal of ophthalmology·2026
Same journal

Identifying patients with poor visual outcomes after primary rhegmatogenous retinal detachment surgery using machine learning.

The British journal of ophthalmology·2026
Same journal

Incidence of bilateral disease and choroidal neovascularisation in punctate inner choroiditis.

The British journal of ophthalmology·2026
Same journal

Reference map of multimodal vision deficits in intermediate age-related macular degeneration: contrast sensitivity and low-contrast visual acuity.

The British journal of ophthalmology·2026
See all related articles

Related Experiment Video

Updated: May 13, 2025

Author Spotlight: Ex Vivo OCT-Based Multimodal Imaging of Human Donor Eyes for Research into Age-Related Macular Degeneration
10:14

Author Spotlight: Ex Vivo OCT-Based Multimodal Imaging of Human Donor Eyes for Research into Age-Related Macular Degeneration

Published on: May 26, 2023

3.0K

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

Xufeng Zhao1,2,3, Chunshi Li4, Jingyuan Yang1,2,3

  • 1Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China.

The British Journal of Ophthalmology
|May 10, 2025
PubMed
Summary
This summary is machine-generated.

Automated natural language processing (NLP) systems show promise in generating diagnostic reports for macular diseases. Both rule-based and deep learning (DL) methods were evaluated against junior ophthalmologists, with AI demonstrating comparable or superior performance in specific areas.

Keywords:
Diagnostic tests/InvestigationMaculaRetina

More Related Videos

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

15.7K
In vivo Structural Assessments of Ocular Disease in Rodent Models using Optical Coherence Tomography
07:44

In vivo Structural Assessments of Ocular Disease in Rodent Models using Optical Coherence Tomography

Published on: July 24, 2020

2.8K

Related Experiment Videos

Last Updated: May 13, 2025

Author Spotlight: Ex Vivo OCT-Based Multimodal Imaging of Human Donor Eyes for Research into Age-Related Macular Degeneration
10:14

Author Spotlight: Ex Vivo OCT-Based Multimodal Imaging of Human Donor Eyes for Research into Age-Related Macular Degeneration

Published on: May 26, 2023

3.0K
A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

15.7K
In vivo Structural Assessments of Ocular Disease in Rodent Models using Optical Coherence Tomography
07:44

In vivo Structural Assessments of Ocular Disease in Rodent Models using Optical Coherence Tomography

Published on: July 24, 2020

2.8K

Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Medical Informatics

Background:

  • Accurate and timely diagnostic reports are crucial for managing macular diseases.
  • Manual report generation can be time-consuming and subject to inter-observer variability.

Purpose of the Study:

  • To compare rule-based and deep learning (DL) natural language processing (NLP) systems for automatically generating diagnostic reports for macular diseases.
  • To evaluate the performance of these AI systems against junior ophthalmologists.

Main Methods:

  • A diagnostic study involving 2261 eyes from 1303 patients with and without macular diseases.
  • Ophthalmic images (fundus photographs, OCT) were analyzed.
  • Rule-based NLP and DL-based NLP systems were developed to generate reports (lesion descriptions, diagnoses, recommendations).
  • Reports were evaluated by junior ophthalmologists and graded by retina specialists on readability, correctness, and recommendations.

Main Results:

  • Rule-based NLP reports outperformed junior ophthalmologists in diagnostic correctness and recommendations.
  • DL-based NLP reports showed slightly lower scores than junior ophthalmologists in lesion description, diagnostic correctness, and recommendations (p<0.05).
  • DL-based NLP reports demonstrated superior readability compared to junior ophthalmologists' reports (p=0.094).

Conclusions:

  • A multimodal AI system combined with NLP algorithms can competently generate diagnostic reports for four macular diseases.
  • These AI systems show potential as valuable tools in ophthalmology for report generation, comparable to junior clinicians.