Specialist vision-language models for clinical ophthalmology

Clinicians spend a significant amount of time reviewing medical images and transcribing their findings regarding patient diagnosis, referral and treatment in text form. Vision-language models (VLMs), which automatically interpret images and summarize their findings as text, have enormous potential to alleviate clinical workloads and increase patient access to high-quality medical care. While foundational models have stirred considerable interest in the medical community, it is unclear whether their general capabilities translate to real-world clinical utility. In this work, we show that foundation VLMs markedly underperform compared to practicing ophthalmologists on specialist tasks crucial to the care of patients with age-related macular degeneration (AMD). To address this, we initially identified the essential capabilities required for image-based clinical decision-making, and then developed a curriculum to selectively train VLMs in these skills. The resulting model, RetinaVLM, can be instructed to write reports that significantly outperform those written by leading foundation medical VLMs in disease staging (F1 score of 0.63 vs. 0.11) and patient referral (0.67 vs. 0.39), and approaches the diagnostic performance of junior ophthalmologists (who achieve 0.77 and 0.78 on the respective tasks). Furthermore, in a reader study involving two senior ophthalmologists with up to 32 years of experience, RetinaVLM's reports were found to be similarly correct (78.6% vs. 82.1%) and complete (both 78.6%) as reports written by junior ophthalmologists with up to 10 years of experience. These results demonstrate that our curriculum-based approach provides a blueprint for specializing generalist foundation medical VLMs to handle real-world clinical tasks.

Clustering disease trajectories in contrastive feature space for biomarker discovery in age-related macular degeneration

Age-related macular degeneration (AMD) is the leading cause of blindness in the elderly. Despite this, the exact dynamics of disease progression are poorly understood. There is a clear need for imaging biomarkers in retinal optical coherence tomography (OCT) that aid the diagnosis, prognosis and management of AMD. However, current grading systems, which coarsely group disease stage into broad categories describing early and intermediate AMD, have very limited prognostic value for the conversion to late AMD. In this paper, we are the first to analyse disease progression as clustered trajectories in a self-supervised feature space. Our method first pretrains an encoder with contrastive learning to project images from longitudinal time series to points in feature space. This enables the creation of disease trajectories, which are then denoised, partitioned and grouped into clusters. These clusters, found in two datasets containing time series of 7,912 patients imaged over eight years, were correlated with known OCT biomarkers. This reinforced efforts by four expert ophthalmologists to investigate clusters, during a clinical comparison and interpretation task, as candidates for time-dependent biomarkers that describe progression of AMD.