Deep Learning for the Prediction of Uncorrected Refractive Error using OCT
Observation-level source data, annotations, or signals. from Source describes OCT-based refractive-error prediction, consistent with human clinical imaging; confirm population wording at addition.
At a glance
| Field | Value |
|---|---|
| Short name | mendeley_deep_learning_prediction_uncorrected_refractive_error |
| Full name | Deep Learning for the Prediction of Uncorrected Refractive Error using OCT |
| First published | 2023-04-05 |
| Publication date precision | day |
| Publication date evidence | data.mendeley.com/datasets |
| Publication date source field | citation_publication_date (version 1) |
| Publication date reviewed | 2026-09-11 |
| Primary category | oct |
| Resource role | current_dataset |
| Dataset family | mendeley_deep_learning_prediction_uncorrected_refractive_error |
| Contained modalities | oct |
| Tasks | prediction |
| Primary reported quantity | Not reported |
| Classes | Not reported (Not reported) |
| Splits | all |
| Size | Not reported |
| Source-stated terms | CC BY 4.0 |
| Normalized terms | cc-by |
| Descriptive screening label | Standard label without an explicit NC clause; not a permission finding |
| Terms scope | dataset_files |
| Access friction | self_service_authenticated |
| Route backend | Mendeley Data |
| Availability | available (checked 2026-08-02) |
| Acquisition support | standard_platform_supported |
| Legacy sample-loader status | Metadata and access only |
Reported quantities
No reproducible primary item count was exposed for the cataloged source version.
Counts retain their source-reported units. Additional rows can describe components, paired items, or derivative copies and are not automatically added to the primary quantity.
Notes
Human provenance: Source describes OCT-based refractive-error prediction, consistent with human clinical imaging; confirm population wording at addition. Source-review finding: Nine files: five JPEG examples, three ZIP archives, and one DOCX.
Access information and download
- CLI
- Python
# Read-only preflight
eyehub download mendeley_deep_learning_prediction_uncorrected_refractive_error --data-dir ./data --dry-run --json
# Download, only when preflight reports supported behavior
eyehub download mendeley_deep_learning_prediction_uncorrected_refractive_error --data-dir ./data
from eyedatahub.acquisition import preflight_dataset
from eyedatahub.datasets.registry import REGISTRY
ds = REGISTRY.get_dataset('mendeley_deep_learning_prediction_uncorrected_refractive_error')
print(preflight_dataset(ds, './data')) # no download
Upstream page: data.mendeley.com/datasets
Source-term evidence: data.mendeley.com/datasets
Loader status
This catalog record provides metadata and access instructions, but it does not yet include a standard DatasetSample loader. Inspect the source file structure or contribute a loader before using it in a training pipeline.
Citation
- BibTeX
- Plain text
@misc{mendeley_deep_learning_prediction_uncorrected_refractive_error,
title = { Deep Learning for the Prediction of Uncorrected Refractive Error using OCT },
note = { Deep Learning for the Prediction of Uncorrected Refractive Error using OCT. Mendeley Data, V2. doi:10.17632/89z7h5gnpw.2 },
url = { https://data.mendeley.com/datasets/89z7h5gnpw/2 },
}
Deep Learning for the Prediction of Uncorrected Refractive Error using OCT. Mendeley Data, V2. doi:10.17632/89z7h5gnpw.2.
Source-stated terms
- Raw source string: CC BY 4.0
- Normalized category:
cc-by - Apparent scope:
dataset_files - Descriptive screening label: Standard label without an explicit NC clause; not a permission finding
⚠️ Source-stated terms, scope, and normalized labels are curation metadata, not legal advice or a permission finding. Review the current official source before transfer or reuse.
Similar resources by shared modality
- syn_oct: SYN-OCT Synthetic Glaucoma OCT Dataset (200,000 images,
cc-by) - multieye: MultiEYE: OCT-Enhanced Fundus Multi-Disease Benchmark (103,959 images,
mit) - eyecare_100k: Eyecare-100K: Multimodal Ophthalmology VQA Corpus (102,000 question answer pairs,
unknown) - kermany_oct: Kermany OCT 2018: Retinal OCT Image Classification (84,484 images,
cc-by) - lmod_plus: LMOD+ Multimodal Ophthalmology Benchmark (32,633 annotated instances,
unknown) - harvard_fairvision: Harvard-FairVision (AMD + DR + Glaucoma, paired SLO + OCT) (30,000 participants,
cc-by-nc-nd) - mario: MARIO: AMD-Progression Longitudinal OCT (MICCAI 2024) (30,000 images,
cc-by) - mmrdr: MMRDR: Multi-Modal Retinal Diabetic Retinopathy Dataset (24,460 images,
cc-by)