Myopia prediction: a systematic review.
- Publisher:
- Springer Nature
- Publication Type:
- Journal Article
- Citation:
- Eye (Lond), 2022, 36, (5), pp. 921-929
- Issue Date:
- 2022-05
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Myopia prediction a systematic review.pdf | Published version | 656.18 kB | Adobe PDF |
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Full metadata record
Field | Value | Language |
---|---|---|
dc.contributor.author | Han, X | |
dc.contributor.author | Liu, C | |
dc.contributor.author | Chen, Y | |
dc.contributor.author | He, M | |
dc.date.accessioned | 2023-07-06T12:03:54Z | |
dc.date.available | 2021-10-01 | |
dc.date.available | 2023-07-06T12:03:54Z | |
dc.date.issued | 2022-05 | |
dc.identifier.citation | Eye (Lond), 2022, 36, (5), pp. 921-929 | |
dc.identifier.issn | 0950-222X | |
dc.identifier.issn | 1476-5454 | |
dc.identifier.uri | http://hdl.handle.net/10453/171275 | |
dc.description.abstract | Myopia is a leading cause of visual impairment and has raised significant international concern in recent decades with rapidly increasing prevalence and incidence worldwide. Accurate prediction of future myopia risk could help identify high-risk children for early targeted intervention to delay myopia onset or slow myopia progression. Researchers have built and assessed various myopia prediction models based on different datasets, including baseline refraction or biometric data, lifestyle data, genetic data, and data integration. Here, we summarize all related work published in the past 30 years and provide a comprehensive review of myopia prediction methods, datasets, and performance, which could serve as a useful reference and valuable guideline for future research. | |
dc.format | Print-Electronic | |
dc.language | eng | |
dc.publisher | Springer Nature | |
dc.relation.ispartof | Eye (Lond) | |
dc.relation.isbasedon | 10.1038/s41433-021-01805-6 | |
dc.rights | info:eu-repo/semantics/closedAccess | |
dc.subject | 1103 Clinical Sciences, 1107 Immunology, 1113 Opthalmology and Optometry | |
dc.subject.classification | Ophthalmology & Optometry | |
dc.subject.classification | 3204 Immunology | |
dc.subject.classification | 3212 Ophthalmology and optometry | |
dc.subject.mesh | Biometry | |
dc.subject.mesh | Child | |
dc.subject.mesh | Disease Progression | |
dc.subject.mesh | Humans | |
dc.subject.mesh | Incidence | |
dc.subject.mesh | Myopia | |
dc.subject.mesh | Refraction, Ocular | |
dc.subject.mesh | Humans | |
dc.subject.mesh | Myopia | |
dc.subject.mesh | Disease Progression | |
dc.subject.mesh | Refraction, Ocular | |
dc.subject.mesh | Incidence | |
dc.subject.mesh | Biometry | |
dc.subject.mesh | Child | |
dc.subject.mesh | Biometry | |
dc.subject.mesh | Child | |
dc.subject.mesh | Disease Progression | |
dc.subject.mesh | Humans | |
dc.subject.mesh | Incidence | |
dc.subject.mesh | Myopia | |
dc.subject.mesh | Refraction, Ocular | |
dc.title | Myopia prediction: a systematic review. | |
dc.type | Journal Article | |
utslib.citation.volume | 36 | |
utslib.location.activity | England | |
utslib.for | 1103 Clinical Sciences | |
utslib.for | 1107 Immunology | |
utslib.for | 1113 Opthalmology and Optometry | |
pubs.organisational-group | /University of Technology Sydney | |
pubs.organisational-group | /University of Technology Sydney/Faculty of Engineering and Information Technology | |
pubs.organisational-group | /University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science | |
utslib.copyright.status | closed_access | * |
dc.date.updated | 2023-07-06T12:03:51Z | |
pubs.issue | 5 | |
pubs.publication-status | Published | |
pubs.volume | 36 | |
utslib.citation.issue | 5 |
Abstract:
Myopia is a leading cause of visual impairment and has raised significant international concern in recent decades with rapidly increasing prevalence and incidence worldwide. Accurate prediction of future myopia risk could help identify high-risk children for early targeted intervention to delay myopia onset or slow myopia progression. Researchers have built and assessed various myopia prediction models based on different datasets, including baseline refraction or biometric data, lifestyle data, genetic data, and data integration. Here, we summarize all related work published in the past 30 years and provide a comprehensive review of myopia prediction methods, datasets, and performance, which could serve as a useful reference and valuable guideline for future research.
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