ArticleNPJ digital medicine2024
Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modeling.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 16 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed.
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- Leveraging large scale deep learning models for diagnosis and visual outcome prediction in retinitis pigmentosa.NPJ digital medicine · 2026Article
- Mind the gap: challenges and future directions for content-based image retrieval in clinical radiology.Frontiers in radiology · 2026Article
- Medical imaging in immunotherapy response evaluation: from RECIST to AI-driven longitudinal models, and the distinction between risk prediction and kinetic diagnosis.Frontiers in medicine · 2026Review
- Artificial Intelligence Applications in Sickle Cell Retinopathy Imaging: Current Progress, Challenges, and Future Directions.Journal of ophthalmology · 2026Review
- Vaccines for microbial eye diseases in the era of data science: opportunities and challenges.Frontiers in immunology · 2026Review
- AMD-Mamba: A Phenotype-Aware Multi-modal Framework for Robust AMD Prognosis.Machine learning in medical imaging. MLMI (Workshop) · 2026Article
- Two-Stage Decoupling Framework for Variable-Length Glaucoma Prognosis.Learning with longitudinal medical images and data : first International Workshop, LMID 2025, held in conjunction with MICCAI 2025, Daejeon, South Korea, September 27, 2025, Proceedings. International Workshop on Learning with Longitudi... · 2026Article
- MIMIC-IV-Ext-22MCTS: A 22 Million-Event Temporal Clinical Time-Series Dataset for Risk Prediction.Research square · 2025Article
- Article
- Chronic Ulcers Healing Prediction through Machine Learning Approaches: Preliminary Results on Diabetic Foot Ulcers Case Study.Journal of clinical medicine · 2025Article
- Time-to-Event Pretraining for 3D Medical Imaging.... International Conference on Learning Representations · 2025Article
- An empirical study of using radiology reports and images to improve intensive care unit mortality prediction.JAMIA open · 2025Article
- The Boundaries of Fair AI in Medical Image Prognosis: A Causal Perspective.Advances in neural information processing systems · 2025Article
- Multimodal data-driven approaches in retinal vein occlusion: A narrative review integrating machine learning and bioinformatics.Advances in ophthalmology practice and researchReview
Corrections and comments
- Erratum issued
- Update of
Authors and funding
12 authors.
Funding
Abstract
Deep learning has enabled breakthroughs in automated diagnosis from medical imaging, with many successful applications in ophthalmology. However, standard medical image classification approaches only assess disease presence at the time of acquisition, neglecting the common clinical setting of longitudinal imaging. For slow, progressive eye diseases like age-related macular degeneration (AMD) and primary open-angle glaucoma (POAG), patients undergo repeated imaging over time to track disease progression and forecasting the future risk of developing a disease is critical to properly plan treatment. Our proposed Longitudinal Transformer for Survival Analysis (LTSA) enables dynamic disease prognosis from longitudinal medical imaging, modeling the time to disease from sequences of fundus photography images captured over long, irregular time periods. Using longitudinal imaging data from the Age-Related Eye Disease Study (AREDS) and Ocular Hypertension Treatment Study (OHTS), LTSA significantly outperformed a single-image baseline in 19/20 head-to-head comparisons on late AMD prognosis and 18/20 comparisons on POAG prognosis. A temporal attention analysis also suggested that, while the most recent image is typically the most influential, prior imaging still provides additional prognostic value.
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.