Rapid Access Macular Screening and Evaluation: A Datasheet for a Dataset to Diagnose and Triage Macular Diseases

Macular disease can cause significant visual morbidity. Timely and accurate diagnosis and management is paramount. However, there is a lack of high-quality multimodal datasets reflective of real-world clinical practice to support research in this area. We present a large longitudinal real-world dataset that aims to address this gap.

Datasheet describing a multimodal dataset of routinely collected ophthalmic data focusing on possible macular disease.

Adult patients (aged $\ge$ 18 years) attending the retina service at Moorfields Eye Hospital National Health Service Foundation Trust for the first time from February 1, 2017 to July 31, 2025 with macula-centered OCT scans (Topcon or Heidelberg).

Data were curated from the INSIGHT Health Data Research Hub, one of the world's largest ophthalmic imaging bioresources, which aims to provide researchers with controlled access to anonymized routinely collected data. Clinical and imaging data derived from routine clinical care were exported, processed, and deidentified for secondary research use.

This datasheet describes the demographic, clinical, and imaging metadata of the dataset, including a transparent overview of its strengths and weaknesses.

Rapid Access Macular Screening and Evaluation (RAMSEs) is a large and diverse real-world dataset. It was specifically designed to facilitate the diagnosis and triage of possible macular disease by providing access to multimodal imaging and clinical metadata. The current version of this dataset (time-locked as of July 2025) consists of retinal images and linked sociodemographic and clinical metadata from 85 444 patients with a median age of 63 (interquartile range 50-75) and a fairly even gender distribution (52.2% female). It comprises >4.9 million multimodal ophthalmic images (e.g. color fundus photographs, ultra-widefield imaging, fundus autofluorescence), including >1.4 million macula-centered OCT scans.

We have developed a large multimodal real-world dataset, which was designed to address existing gaps in dataset size, disease distribution, and key clinical and sociodemographic metadata for possible macular disease. This valuable longitudinal resource can serve multiple purposes, including the development or robust clinical validation of artificial intelligence models, facilitating insights into real-world patient pathways and outcomes, or enabling research relating to epidemiology or big data analytics. This dataset may be made available through INSIGHT via a structured application process.

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.