Evidence map›Paper›PMID 42699395›Full record

ArticleData in brief2026

Orbscan IIz multimodal corneal topography dataset of anterior axial power maps with structured clinical parameters for unsupervised and self-supervised representation learning.

Ali Mustafa Ali Alshaykha, Qabas A Hameed, Mohammed Basim Omar

Abstract read
In one paragraph

Article in Data in brief, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Ali Mustafa Ali AlshaykhaCollege of Basic Education, Al-Shirqat, University of Tikrit, Tikrit, Iraq.
Qabas A HameedCollege of Computer Science and Mathematics, University of Tikrit, Tikrit, Iraq.
Mohammed Basim OmarCollege of Computer Science and Mathematics, University of Tikrit, Tikrit, Iraq.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We present a multimodal corneal topography dataset comprising 3000 anterior axial power maps acquired from 3000 unique patients using the Orbscan IIz slit-scanning device (Bausch & Lomb) at a single private ophthalmology clinic in Iraq. The examinations were performed during routine clinical practice between 2007 and 2025 using the same diagnostic device throughout the acquisition period. Each image is paired with 19 structured clinical parameter fields extracted from the original device output using an automated OCR-based pipeline. These parameters include simulated keratometry astigmatism (SimK_Astig), maximum and minimum corneal power (MaxK and MinK), zone-specific irregularity indices, mean power, astigmatic power, steep and flat axis orientations across 3 mm and 5 mm optical apertures, corneal pachymetry at the thinnest point with spatial coordinates, anterior chamber depth, white-to-white diameter, pupil diameter, and angle kappa with intercept coordinates. All images were fully anonymized, cropped to the central map region, resized to 1024 × 1024 pixels, and saved in lossless PNG format. Following a quality control pipeline assessing image integrity, metadata completeness, and anatomical plausibility, 2633 image-metadata pairs satisfied the predefined inclusion criteria and constitute the quality-controlled subset characterized in this article. The dataset is released without diagnostic labels by design, positioning it as a foundation for unsupervised, self-supervised, and semi-supervised representation learning in anterior segment analysis. The complete dataset is available on Mendeley Data (DOI: 10.17,632/78wt2nc387.3) under a CC BY-NC 4.0 license.

Indexed as

Artificial intelligenceImage preprocessingMachine learningOpen research dataOphthalmic imagingOptical character recognition

Identifiers

PMID42699395
PMCPMC13543107

What OpenQuestion holds

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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 OpenQuestion graph.