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

Ischemic Stroke l: Introduction01:15

Ischemic Stroke l: Introduction

57
Ischemic stroke is an acute cerebrovascular condition in which blood flow to a brain region is suddenly interrupted, leading to tissue infarction. Neurons depend on continuous oxygen and glucose supply, so even brief reductions in perfusion cause energy failure, ionic imbalance, and irreversible injury. Ischemic strokes are classified into thrombotic and embolic types based on their underlying mechanisms.Thrombotic MechanismsThrombotic stroke develops when a clot forms within a cerebral artery.
57
Hemorrhagic Stroke l: Introduction01:17

Hemorrhagic Stroke l: Introduction

41
A hemorrhagic stroke is an acute neurological event that occurs when a weakened cerebral blood vessel ruptures, allowing blood to accumulate within or around the brain. The sudden release of blood forms a focal hematoma that increases intracranial pressure, displaces neural tissue, and can obstruct cerebrospinal fluid pathways. These effects may be compounded by intraventricular extension of the hemorrhage, cerebral edema, or compression of adjacent structures, all of which contribute to...
41
Transient Ischemic Attack l: Introduction01:26

Transient Ischemic Attack l: Introduction

35
A transient ischemic attack (TIA) is a brief episode of neurological dysfunction caused by a temporary, focal reduction in cerebral blood flow. Although symptoms resemble those of an ischemic stroke, the interruption in perfusion is short-lived and does not cause permanent infarction. TIAs are clinically important because they often serve as early warning events for future stroke.Mechanisms of Transient Cerebral IschemiaTransient cerebral ischemia may arise through several mechanisms. One...
35

You might also read

Related Articles

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

Sort by
Same author

High-fidelity fast fluorescence lifetime imaging by event-based denoising.

Nature biotechnology·2026
Same author

High numerical aperture confocal volumetric mesoscope reveals mesoscale subcellular dynamics in vivo.

Nature biotechnology·2026
Same author

Depletion of <i>Blautia wexlerae</i> and <i>Parabacteroides distasonis</i> in adiposity-related prehypertension.

Frontiers in microbiology·2026
Same authorSame journal

Small language models in medicine.

Nature biomedical engineering·2026
Same author

LMOD+: A Comprehensive Multimodal Dataset and Benchmark for Developing and Evaluating Multimodal Large Language Models in Ophthalmology.

ACM transactions on computing for healthcare·2026
Same authorSame journal

Neuro-symbolic artificial intelligence in medicine.

Nature biomedical engineering·2026

Related Experiment Video

Updated: May 6, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

43.4K

A deep learning system for detecting silent brain infarction and predicting stroke risk.

Nan Jiang1,2,3, Hongwei Ji3, Zhouyu Guan1

  • 1Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Institute for Proactive Healthcare, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, China.

Nature Biomedical Engineering
|June 6, 2025
PubMed
Summary

A new deep learning system, DeepRETStroke, uses retinal images to detect silent brain infarctions (SBIs) and predict stroke risk. This AI tool offers a non-invasive alternative to brain imaging for stroke prevention strategies.

More Related Videos

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
10:25

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

49.3K
Evaluation of the Cognitive Performance of Hypertensive Patients with Silent Cerebrovascular Lesions
07:30

Evaluation of the Cognitive Performance of Hypertensive Patients with Silent Cerebrovascular Lesions

Published on: April 23, 2021

3.4K

Related Experiment Videos

Last Updated: May 6, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

43.4K
Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
10:25

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

49.3K
Evaluation of the Cognitive Performance of Hypertensive Patients with Silent Cerebrovascular Lesions
07:30

Evaluation of the Cognitive Performance of Hypertensive Patients with Silent Cerebrovascular Lesions

Published on: April 23, 2021

3.4K

Area of Science:

  • Ophthalmology and Neurology
  • Artificial Intelligence in Medicine
  • Cardiovascular Disease Research

Background:

  • Current brain imaging methods for detecting silent brain infarctions (SBIs) are not widely accessible to the general population.
  • Silent brain infarctions are significant risk factors for future stroke events.
  • There is a need for non-invasive, scalable methods to identify individuals at high risk of stroke.

Purpose of the Study:

  • To develop and validate a deep learning system, DeepRETStroke, utilizing retinal images for the detection of SBIs.
  • To assess DeepRETStroke's capability in predicting both incident and recurrent stroke events.
  • To compare the efficacy of DeepRETStroke with traditional clinical traits in guiding stroke recurrence prevention.

Main Methods:

  • Pretraining the DeepRETStroke system on a large dataset of 895,640 retinal photographs to establish an eye-brain connection foundation model.
  • Validating DeepRETStroke's performance on diverse datasets comprising 213,762 retinal photographs across multiple countries for SBI detection and stroke event prediction.
  • Conducting a prospective study with 218 stroke participants to evaluate DeepRETStroke against clinical traits for stroke recurrence prevention strategies.

Main Results:

  • DeepRETStroke demonstrated strong performance in internal validation, achieving an area under the curve (AUC) of 0.901 for incident stroke prediction and 0.769 for recurrent stroke prediction.
  • Consistent performance was observed across diverse external validation datasets, confirming the system's generalizability.
  • In a prospective study, DeepRETStroke proved superior to clinical traits in predicting stroke events, particularly when incorporating SBI detection.

Conclusions:

  • DeepRETStroke offers a feasible and effective non-invasive method for detecting silent brain infarctions using retinal images.
  • The system demonstrates significant potential for improving stroke risk prediction and guiding personalized prevention strategies.
  • Retinal image-based deep learning presents a promising advancement in cardiovascular disease research and stroke management, bypassing the need for brain imaging.