BACKGROUND: Keratoconus is a disorder characterized by progressive thinning and distortion of the cornea. If detected at an early stage, corneal collagen cross-linking can prevent disease progression and further visual loss. Although advanced forms are easily detected, reliable identification of subclinical disease can be problematic. Several different machine learning algorithms have been used to improve the detection of subclinical keratoconus based on the analysis of multiple types of clinical measures, such as corneal imaging, aberrometry, or biomechanical measurements. OBJECTIVE: The aim of this study is to survey and critically evaluate the literature on the algorithmic detection of subclinical keratoconus and equivalent definitions. METHODS: For this systematic review, we performed a structured search of the following databases: MEDLINE, Embase, and Web of Science and Cochrane Library from January 1, 2010, to October 31, 2020. We included all full-text studies that have used algorithms for the detection of subclinical keratoconus and excluded studies that did not perform validation. This systematic review followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) recommendations. RESULTS: We compared the measured parameters and the design of the machine learning algorithms reported in 26 papers that met the inclusion criteria. All salient information required for detailed comparison, including diagnostic criteria, demographic data, sample size, acquisition system, validation details, parameter inputs, machine learning algorithm, and key results are reported in this study. CONCLUSIONS: Machine learning has the potential to improve the detection of subclinical keratoconus or early keratoconus in routine ophthalmic practice. Currently, there is no consensus regarding the corneal parameters that should be included for assessment and the optimal design for the machine learning algorithm. We have identified avenues for further research to improve early detection and stratification of patients for early treatment to prevent disease progression.
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
Steroid 5α-reductase type 3 congenital disorder of glycosylation (SRD5A3-CDG) is a severe metabolic disease manifesting as muscle hypotonia, developmental delay, cerebellar ataxia and ocular symptoms; typically, nystagmus and optic disc pallor. Recently, early onset retinal dystrophy has been reported as an additional feature. In this study, we summarize ocular phenotypes and SRD5A3 variants reported to be associated with SRD5A3-CDG. We also describe in detail the ophthalmic findings in a 12-year-old Czech child harbouring a novel homozygous variant, c.436G>A, p.(Glu146Lys) in SRD5A3. The patient was reviewed for congenital nystagmus and bilateral optic neuropathy diagnosed at 13 months of age. Examination by spectral domain optical coherence tomography and fundus autofluorescence imaging showed clear signs of retinal dystrophy not recognized until our investigation. Best corrected visual acuity was decreased to 0.15 and 0.16 in the right and left eye, respectively, with a myopic refractive error of -3.0 dioptre sphere (DS) / -2.5 dioptre cylinder (DC) in the right and -3.0 DS / -3.0 DC in the left eye. The proband also had optic head nerve drusen, which have not been previously observed in this syndrome.
- MeSH
- 3-oxo-5-alfa-steroid-4-dehydrogenasa chemie genetika MeSH
- dítě MeSH
- fenotyp MeSH
- homozygot MeSH
- lidé MeSH
- membránové proteiny chemie genetika MeSH
- mutace genetika MeSH
- oči patologie MeSH
- rodokmen MeSH
- sekvence aminokyselin MeSH
- sekvence nukleotidů MeSH
- vrozené poruchy glykosylace enzymologie genetika MeSH
- Check Tag
- dítě MeSH
- lidé MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- kazuistiky MeSH
- přehledy MeSH
