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Updated: Jun 9, 2026

In vivo Structural Assessments of Ocular Disease in Rodent Models using Optical Coherence Tomography
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Published on: July 24, 2020

A Datasheet for the INSIGHT Moorfields Cornea Anterior Segment Dataset (CADMUS).

Shafi Balal1,2, Zaid Alsafi1, Marcello Leucci1

  • 1Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom.

Ophthalmology Science
|June 8, 2026
PubMed
Summary

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The Cornea Anterior Segment Dataset from Moorfields via INSIGHT (CADMUS) offers a large, multimodal imaging and clinical data resource for corneal research. This dataset supports advancements in artificial intelligence and understanding of anterior segment diseases.

Area of Science:

  • Ophthalmology and Vision Science
  • Medical Imaging
  • Health Informatics

Background:

  • The anterior segment of the eye is crucial for vision, and its pathologies require extensive research.
  • Developing advanced diagnostic and treatment strategies necessitates large-scale, multimodal datasets.

Purpose of the Study:

  • To describe the Cornea Anterior Segment Dataset from Moorfields via INSIGHT (CADMUS).
  • To provide a comprehensive resource for corneal research and artificial intelligence (AI) development.
  • To facilitate studies on anterior segment diseases and surgical outcomes.

Main Methods:

  • Dataset derived from routine clinical care at Moorfields Eye Hospital NHS Foundation Trust.
  • Includes anonymized anterior segment imaging (MS-39 Placido, OCT, photographs) and linked clinical metadata.
Keywords:
Anterior segment imagingArtificial intelligenceCorneaDatasetOCT

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  • Data pseudonymized, standardized, and curated within the INSIGHT Secure Research Environment.

    • Main Results:

    • CADMUS comprises over 945,000 image acquisitions from 22,482 patients.
    • Contains extensive data on diverse pathologies like keratoconus, cataract, and corneal scars.
    • Includes detailed records of common anterior segment surgeries and longitudinal follow-up data.

    Conclusions:

    • CADMUS is a substantial, longitudinal, multimodal dataset linking imaging with clinical metadata.
    • It serves as a robust platform for reproducible research in anterior segment disease.
    • Facilitates AI development, longitudinal modeling, and surgical outcomes analysis.