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

You might also read

Related Articles

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

Sort by

Same author

Evaluation of Large Language Models for Structured Data Extraction From Interstitial Lung Disease Clinical Notes: Comparative Study.

Journal of medical Internet research·2026

Same author

Evaluating the robustness and readiness of large frontier models in health AI applications.

Nature medicine·2026

Same author

Neural Shape Modeling Reveals Early and Progressive Femoral Bone Shape and Cartilage Thickness Changes After Anterior Cruciate Ligament Reconstruction.

medRxiv : the preprint server for health sciences·2026

Same author

Co-intelligence: a proposal for human-artificial intelligence collaboration for large language models in medical research.

The Lancet. Digital health·2026

Same author

Artificial Intelligence in Peripheral Artery Disease: A Science Advisory From the American Heart Association.

Circulation. Population health and outcomes·2026

Same author

Physician-Reported Safety Outcomes of AI-Generated Hospital Course Summaries.

JAMA network open·2026

Same journal

Continual Slow-and-Fast Adaptation of Latent Neural Dynamics (CoSFan): Meta-Learning What-How & When to Adapt.

... International Conference on Learning Representations·2026

Same journal

Topology-Aware Segmentation Using Discrete Morse Theory.

... International Conference on Learning Representations·2026

Same journal

TOPODIFFUSIONNET: A TOPOLOGY-AWARE DIFFUSION MODEL.

... International Conference on Learning Representations·2026

Same journal

GEOMETRY OF LONG-TAILED REPRESENTATION LEARNING: REBALANCING FEATURES FOR SKEWED DISTRIBUTIONS.

... International Conference on Learning Representations·2026

Same journal

Probabilistic Geometric Principal Component Analysis with application to neural data.

... International Conference on Learning Representations·2026

Same journal

BRAID: Input-driven nonlinear dynamical modeling of neural-behavioral data.

... International Conference on Learning Representations·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 4, 2026

Development and Evaluation of 3D-Printed Cardiovascular Phantoms for Interventional Planning and Training

Development and Evaluation of 3D-Printed Cardiovascular Phantoms for Interventional Planning and Training

Published on: January 18, 2021

Time-to-Event Pretraining for 3D Medical Imaging.

Zepeng Huo¹, Jason Alan Fries¹, Alejandro Lozano²

¹Center for Biomedical Informatics Research, Stanford University.

... International Conference on Learning Representations

|March 3, 2026

Summary

This summary is machine-generated.

New pretraining integrates 3D imaging with electronic health records (EHRs) to predict disease risk. This approach overcomes the missing context problem, improving outcome prediction and identifying novel imaging biomarkers.

More Related Videos

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

Published on: May 20, 2016

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: Mar 4, 2026

Development and Evaluation of 3D-Printed Cardiovascular Phantoms for Interventional Planning and Training

Development and Evaluation of 3D-Printed Cardiovascular Phantoms for Interventional Planning and Training

Published on: January 18, 2021

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

Published on: May 20, 2016

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:

Artificial Intelligence
Medical Imaging
Biomedical Informatics

Background:

Medical foundation models and imaging data are growing.
Current self-supervised methods for 3D imaging models capture local features but miss temporal context for long-term outcome prediction.
A missing context problem limits linking imaging biomarkers to disease progression.

Purpose of the Study:

To introduce time-to-event pretraining for 3D medical imaging models.
To leverage longitudinal electronic health records (EHRs) for temporal supervision.
To identify imaging biomarkers predictive of future disease risk.

Main Methods:

Developed a pretraining framework using paired, longitudinal EHR data with 18,945 CT scans.
Utilized time-to-event distributions across thousands of EHR-derived tasks for supervision.
Evaluated performance on 8 benchmark outcome prediction tasks.

Main Results:

Achieved an average AUROC increase of 23.7% in outcome prediction.
Demonstrated a 29.4% gain in Harrell's C-index across benchmark tasks.
Maintained diagnostic classification performance.

Conclusions:

Time-to-event pretraining effectively integrates longitudinal EHR and 3D imaging data.
The method enhances clinical risk prediction by identifying imaging biomarkers linked to long-term health outcomes.
This approach advances the development of medical foundation models for disease risk assessment.