Response-Aware Multimodal Learning for Post-Treatment Visual Acuity Forecasting

• URL: http://arxiv.org/abs/2606.00588v1
• Date: Sat, 30 May 2026 07:36:28 GMT
• Title: Response-Aware Multimodal Learning for Post-Treatment Visual Acuity Forecasting
• Abstract summary: Long-term visual acuity (VA) outcomes after anti-VEGF therapy are central to patient counseling, expectation setting, and follow-up planning.<n>We propose ReVA, a response-aware multimodal framework that integrates structural features from baseline and month-1 OCT.<n>Our findings show that incorporating early treatment-response signals enables clinically meaningful long-term visual acuity forecasting.
• Score: 2.596631029172601
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Long-term visual acuity (VA) outcomes after anti-VEGF therapy are central to patient counseling, expectation setting, and follow-up planning in diabetic macular edema (DME). However, in clinical practice, physicians must often estimate long-term visual trajectories based only on early post-treatment findings, making reliable prognostication difficult. Although prior OCT-based learning approaches have largely focused on short-term response or single-endpoint prediction, modeling VA trajectories across multiple future time points from early longitudinal observations remains insufficiently explored. In this study, we assembled a real-world cohort of 188 anti-VEGF-treated DME patients with paired baseline and month-1 OCT scans, along with tabular OCT-derived biomarkers and non-imaging clinical variables. Using only these early data, we formulate a multi-horizon VA forecasting problem aimed at predicting visual outcomes at 3, 6, 12, 18, and 24 months, reflecting clinically meaningful follow-up intervals. We propose ReVA, a response-aware multimodal framework that integrates structural features from baseline and month-1 OCT with the tabular variables to capture baseline disease status and early treatment response. ReVA uses spatial attention to preserve localized prognostic imaging features and a dependency-aware tabular encoder to model interactions among clinical variables. These multimodal representations are fused to predict patient-specific long-term visual acuity trajectories. The proposed framework achieves MAE=0.1246, RMSE=0.1621, and R^2=0.6064 for 24-month VA prediction, with consistent performance across all forecast horizons. Our findings show that incorporating early treatment-response signals enables clinically meaningful long-term visual acuity forecasting, supporting data-driven decision support for routine anti-VEGF management.

Related papers

• How Good are Foundation Models in Longitudinal MRI Disease Progression Reasoning? [49.14050864978079]
  We introduce the Time-Aware Multi-View MRI Benchmark, an evaluation framework unifying multi-view anatomical input, temporal reasoning, and structured localization guidance.<n>The benchmark comprises 3,920 expert-verified question-answer pairs derived from 890 patients across over 3,200 longitudinal MRI timepoints.
  arXiv  Detail & Related papers  (2026-08-13T14:35:20Z)
• TRACE-PCa: Predicting Prostate Cancer Progression from Longitudinal MRI During Active Surveillance [3.0383869408727864]
  Active surveillance is the preferred strategy for favorable-risk prostate cancer.<n>Current protocols rely on scheduled repeat biopsies, most of which reveal no progression and are unnecessary.<n>We propose an end-to-end temporal and multimodal model for predicting pathological progression during AS without lesion segmentation.
  arXiv  Detail & Related papers  (2026-07-15T07:00:50Z)
• Graph Representation Learning of Longitudinal Medical Imaging Trajectories for Treatment Response Prediction [2.9823166626381212]
  In patients with breast cancer, pathological complete response (pCR) has been established as a clinically meaningful surrogate marker for long-term outcomes.<n>We propose an imaging-based 3D-temporal framework for treatment response prediction that integrates a state-of-the-art neural graph with temporal modeling.<n>Our study substantiates the utility of clinically meaningful longitudinal medical imagaging modeling for predicting NACT-induced pCR.
  arXiv  Detail & Related papers  (2026-07-06T10:39:14Z)
• Neural Architecture for Fast and Reliable Coagulation Assessment in Clinical Settings: Leveraging Thromboelastography [11.141462411413059]
  Real-time coagulation monitoring can enable early detection and prompt remediation of risks.<n>Traditional Thromboelastography (TEG) can only provide such outputs after nearly 1 hour of measurement.<n>We present Physiological State Reconstruc-tion (PSR), a new algorithm specifically designed to take ad-vantage of dynamic changes between individuals.
  arXiv  Detail & Related papers  (2026-01-12T15:03:53Z)
• Epistemic-aware Vision-Language Foundation Model for Fetal Ultrasound Interpretation [83.02147613524032]
  We introduce FetalMind, a medical AI system tailored to fetal ultrasound for both report generation and diagnosis.<n>We propose Salient Epistemic Disentanglement (SED), which injects an expert-curated bipartite graph into the model to decouple view-disease associations.<n>FetalMind outperforms open- and closed-source baselines across all gestational stages, achieving +14% average gains and +61.2% higher accuracy on critical conditions.
  arXiv  Detail & Related papers  (2025-10-14T19:57:03Z)
• AI-based response assessment and prediction in longitudinal imaging for brain metastases treated with stereotactic radiosurgery [1.3844235712582453]
  Brain Metastases (BM) are a large contributor to mortality of patients with cancer.<n>They are treated with Stereotactic Radiosurgery (SRS) and monitored with Magnetic Resonance Imaging (MRI) at regular follow-up intervals according to treatment guidelines.<n> Response to treatment in longitudinal imaging is being studied, to better understand growth trajectories and predict treatment success or toxicity as early as possible.<n>In this study, we implement an automated pipeline to curate a large longitudinal dataset of SRS treatment data, resulting in a cohort of 896 BMs in 177 patients who were monitored for >360 days at approximately two-month intervals at Lausanne
  arXiv  Detail & Related papers  (2025-09-08T07:29:45Z)
• CTPD: Cross-Modal Temporal Pattern Discovery for Enhanced Multimodal Electronic Health Records Analysis [50.56875995511431]
  We introduce a Cross-Modal Temporal Pattern Discovery (CTPD) framework, designed to efficiently extract meaningful cross-modal temporal patterns from multimodal EHR data.<n>Our approach introduces shared initial temporal pattern representations which are refined using slot attention to generate temporal semantic embeddings.
  arXiv  Detail & Related papers  (2024-11-01T15:54:07Z)
• Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modeling [49.52787013516891]
  Our proposed Longitudinal Transformer for Survival Analysis (LTSA) enables dynamic disease prognosis from longitudinal medical imaging. A temporal attention analysis also suggested that, while the most recent image is typically the most influential, prior imaging still provides additional prognostic value.
  arXiv  Detail & Related papers  (2024-05-14T17:15:28Z)
• Visual Acuity Prediction on Real-Life Patient Data Using a Machine Learning Based Multistage System [0.40151799356083057]
  The prediction of the visual acuity (VA) and the earliest possible detection of deterioration under real-life conditions is challenging due to heterogeneous and incomplete data. We present a workflow for the development of a research-compatible data corpus fusing different IT systems of the department of ophthalmology of a German maximum care hospital. We achieve a final prediction accuracy of 69 % in macro average F1-score, while being in the same range as the ophthalmologists with 57.8 and 50 +- 10.7 % F1-score.
  arXiv  Detail & Related papers  (2022-04-25T21:20:27Z)
• MIA-Prognosis: A Deep Learning Framework to Predict Therapy Response [58.0291320452122]
  This paper aims at a unified deep learning approach to predict patient prognosis and therapy response. We formalize the prognosis modeling as a multi-modal asynchronous time series classification task. Our predictive model could further stratify low-risk and high-risk patients in terms of long-term survival.
  arXiv  Detail & Related papers  (2020-10-08T15:30:17Z)
• BiteNet: Bidirectional Temporal Encoder Network to Predict Medical Outcomes [53.163089893876645]
  We propose a novel self-attention mechanism that captures the contextual dependency and temporal relationships within a patient's healthcare journey. An end-to-end bidirectional temporal encoder network (BiteNet) then learns representations of the patient's journeys. We have evaluated the effectiveness of our methods on two supervised prediction and two unsupervised clustering tasks with a real-world EHR dataset.
  arXiv  Detail & Related papers  (2020-09-24T00:42:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.