Clinicopathologic correlation of aniridia: Optical coherence tomography angiography and histopathologic observations

Therattil, Anthony; Naranjo, Andrea; Hsu, S. Tammy; Kelly, Michael; Freedman, Sharon F.; Dubovy, Sander R.; Vajzovic, Lejla

doi:10.1016/j.ajoc.2020.100919

Cited by 3 publications

(4 citation statements)

References 14 publications

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“…This area of thinning coincided with decreased capillary flow in the superficial (SVC) and deep vascular complexes (DVC) on OCT-A (Figure 2, A and B). Similar to what was previously reported in an infant with retinopathy of prematurity 8 and a child with aniridia, 9 we observed "diving" of superficial retinal vessels across the inner plexiform layer (IPL)/inner nuclear layer (INL) border (Figure 2). Notably, the diving occurred as the vessels traverse from the focal area of retinal thinning to the surrounding normal-thickness retina (Figure 2C).…”

Section: Case Reportsupporting

confidence: 85%

“…So far, two cases have been reported in an 11-month-old preterm infant with retinopathy of prematurity 8 and a 7-year-old child with aniridia. 9 Our case adds IP to this list of pediatric retinal vascular diseases with this phenotype and suggests that this aberrance may occur more often than reported. Interestingly, our case demonstrates that such “diving” occurs at locations where medium-sized superficial vessels traverse from thin retina to retina with normal thickness.…”

Section: Discussionmentioning

confidence: 51%

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Macular Neurovascular Abnormalities in a Child With Incontinentia Pigmenti on Handheld Optical Coherence Tomography Angiography

Patel

Tai

Imperio

et al. 2023

RETINAL Cases &Amp; Brief Reports

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Purpose: To report macular neurovascular abnormalities in a child with incontinentia pigmenti using handheld optical coherence tomography (OCT) and OCT angiography (OCT-A).Methods: An eye of a child with incontinentia pigmenti enrolled in BabySTEPS was imaged using an investigational noncontact, handheld swept-source OCT device during examination under anesthesia. Custom MATLAB scripts were used to generate depthresolved vascular slabs, B-scans with flow overlay, and retinal thickness maps.Results: Depth-resolved OCT and OCT-A imaging demonstrated focal areas of decreased capillary flow that corresponded to areas of both inner retinal and outer retinal thinning on retinal thickness maps. Atypical diving of superficial retinal vessels occurred as they traversed from thin retina to normal-thickness retina.Conclusion: Depth-resolved OCT and OCT-A identified retinal vascular abnormalities that were not evident on fundus photography or fluorescein angiography. This case depicted concurrent, localized abnormalities in retinal thickness and microvasculature in an eye with incontinentia pigmenti.

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Section: Case Reportsupporting

confidence: 85%

Section: Discussionmentioning

confidence: 51%

Macular Neurovascular Abnormalities in a Child With Incontinentia Pigmenti on Handheld Optical Coherence Tomography Angiography

Patel

Tai

Imperio

et al. 2023

RETINAL Cases &Amp; Brief Reports

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“…[ 26 ] Recently, OCT angiography in a 7-year-old child revealed the absence of FAZ and dipping of vessels from the superficial vascular complex to the deep vascular complex. [ 27 ] Further studies may reveal the importance of retinal microvascular development in the pathogenesis of aniridia. Both multifocal and full-field electroretinography (ERG) have been shown to be affected in the majority of aniridia patients.…”

Section: Clinical Featuresmentioning

confidence: 99%

Clinical and molecular aspects of congenital aniridia – A review of current concepts

Tibrewal¹,

Ratna²,

Gour³

et al. 2022

Indian Journal of Ophthalmology

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Congenital aniridia is a pan ocular disorder characterized by partial or total loss of iris tissue as the defining feature. Classic aniridia, however, has a spectrum of ocular findings, including foveal hypoplasia, optic nerve hypoplasia, nystagmus, late-onset cataract, glaucoma, and keratopathy. The latter three are reasons for further visual compromise in such patients. This entity is often due to mutations in the PAX6 (Paired box protein Pax-6) gene. Recently, aniridia-like phenotypes have been reported due to non- PAX6 mutations as in PITX2, FOXC1, FOXD3, TRIM44 , and CYP1B1 as well wherein there is an overlap of aniridia, such as iris defects with congenital glaucoma or anterior segment dysgenesis. In this review, we describe the various clinical features of classic aniridia, the comorbidities and their management, the mutation spectrum of the genes involved, genotype-phenotype correlation of PAX6 and non- PAX6 mutations, and the genetic testing plan. The various systemic associations and their implications in screening and genetic testing have been discussed. Finally, the future course of aniridia treatment in the form of drugs (such as ataluren) and targeted gene therapy has been discussed.

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“…Within the fovea, there is a region free from microvascular structure, giving a name as, “foveal avascular zone (FAZ)’’. The dimensions of FAZ (such as area, perimeter, and shape/circularity index) were related to different diseases such as DR, Glaucoma, sickle cell, albinism, idiopathic foveal hypoplasia, aniridia, Fabry, Alzheimer, individuals born prematurely, visual acuity, and e-cigarette smoking 2 , 4 – 19 . Therefore, methods for precise measurement of FAZ dimensions are gaining much interest.…”

Section: Introductionmentioning

confidence: 99%

A lightweight deep learning model for automatic segmentation and analysis of ophthalmic images

Sharma

Ninomiya

Omodaka

et al. 2022

Sci Rep

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Detection, diagnosis, and treatment of ophthalmic diseases depend on extraction of information (features and/or their dimensions) from the images. Deep learning (DL) model are crucial for the automation of it. Here, we report on the development of a lightweight DL model, which can precisely segment/detect the required features automatically. The model utilizes dimensionality reduction of image to extract important features, and channel contraction to allow only the required high-level features necessary for reconstruction of segmented feature image. Performance of present model in detection of glaucoma from optical coherence tomography angiography (OCTA) images of retina is high (area under the receiver-operator characteristic curve AUC ~ 0.81). Bland–Altman analysis gave exceptionally low bias (~ 0.00185), and high Pearson’s correlation coefficient (p = 0.9969) between the parameters determined from manual and DL based segmentation. On the same dataset, bias is an order of magnitude higher (~ 0.0694, p = 0.8534) for commercial software. Present model is 10 times lighter than Unet (popular for biomedical image segmentation) and have a better segmentation accuracy and model training reproducibility (based on the analysis of 3670 OCTA images). High dice similarity coefficient (D) for variety of ophthalmic images suggested it’s wider scope in precise segmentation of images even from other fields. Our concept of channel narrowing is not only important for the segmentation problems, but it can also reduce number of parameters significantly in object classification models. Enhanced disease diagnostic accuracy can be achieved for the resource limited devices (such as mobile phone, Nvidia’s Jetson, Raspberry pi) used in self-monitoring, and tele-screening (memory size of trained model ~ 35 MB).

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Clinicopathologic correlation of aniridia: Optical coherence tomography angiography and histopathologic observations

Cited by 3 publications

References 14 publications

Macular Neurovascular Abnormalities in a Child With Incontinentia Pigmenti on Handheld Optical Coherence Tomography Angiography

Macular Neurovascular Abnormalities in a Child With Incontinentia Pigmenti on Handheld Optical Coherence Tomography Angiography

Clinical and molecular aspects of congenital aniridia – A review of current concepts

A lightweight deep learning model for automatic segmentation and analysis of ophthalmic images

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