Ethnicity and ocular imaging

Bourne, Rupert

doi:10.1038/eye.2010.187

Cited by 24 publications

(16 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CFP is influenced by the pigmentation of the fundus. [ 11 ] Higher pigmentation of the fundus in our case due to ethnic difference from those described by Muftuoglu et al . may be a reason of smaller size of nevus on CFP compared to MCI.…”

Section: Discussioncontrasting

confidence: 41%

Measurement of size of pigmented choroidal nevus: Superiority of multicolor imaging compared to conventional color fundus photography

Saurabh

Roy

Sinharoy

et al. 2018

Indian J Ophthalmol

View full text Add to dashboard Cite

Choroidal nevi are benign fundus lesions that require regular follow with documentation. Conventional color fundus photography (CFP) has traditionally been used to images these lesions. Multicolor imaging (MCI) available on Spectralis spectral domain optical coherence tomography system is increasingly been tested vis-à-vis conventional CFP in various retinal diseases. We present data of the right eye of a 59-year-old gentleman with choroidal nevus who underwent conventional CFP as well as MCI. Nevus appeared orange red on MCI and its size appeared larger than the same measured on conventional CFP. We also report infrared reflectance and near infrared autofluorescence features of choroidal nevus.

show abstract

Section: Discussioncontrasting

confidence: 41%

Measurement of size of pigmented choroidal nevus: Superiority of multicolor imaging compared to conventional color fundus photography

Saurabh

Roy

Sinharoy

et al. 2018

Indian J Ophthalmol

View full text Add to dashboard Cite

show abstract

“…Studies have shown that retinal structures captured within color images as well as the phenotype and prevalence of some ocular diseases varied with ethnicity. 68,69 For example, pigmentation variations among ethnic groups can affect the observed ocular image. 70 Giancardo et al 71 have noticed that color retinal images of Caucasians have a strong red component whereas those of African Americans had a much stronger blue component.…”

Section: Illumination and Homogeneity Algorithms Analysismentioning

confidence: 99%

Retinal image quality assessment based on image clarity and content

et al. 2016

View full text Add to dashboard Cite

Retinal image quality assessment (RIQA) is an essential step in automated screening systems to avoid misdiagnosis caused by processing poor quality retinal images. A no-reference transform-based RIQA algorithm is introduced that assesses images based on five clarity and content quality issues: sharpness, illumination, homogeneity, field definition, and content. Transform-based RIQA algorithms have the advantage of considering retinal structures while being computationally inexpensive. Wavelet-based features are proposed to evaluate the sharpness and overall illumination of the images. A retinal saturation channel is designed and used along with wavelet-based features for homogeneity assessment. The presented sharpness and illumination features are utilized to assure adequate field definition, whereas color information is used to exclude nonretinal images. Several publicly available datasets of varying quality grades are utilized to evaluate the feature sets resulting in area under the receiver operating characteristic curve above 0.99 for each of the individual feature sets. The overall quality is assessed by a classifier that uses the collective features as an input vector. The classification results show superior performance of the algorithm in comparison to other methods from literature. Moreover, the algorithm addresses efficiently and comprehensively various quality issues and is suitable for automatic screening systems.

show abstract

“…The Article describes a tested methodology using convolutional neural networks and its effectiveness in reading images from African patients. Although Bellemo and colleagues' final ensemble model had not been previously trained on African retinas, 7 which are more pigmented than Asian or white retinas, 8 the results they report are promising, showing the AI model and human graders to have similar outcomes in detection of referable diabetic retinopathy and systemic risk factor associations. The area under the curve of the AI system for referable diabetic retinopathy was 0·973 (95% CI 0·969-0·978), with corresponding sensitivity of 92·25% (90·10-94·12) and specificity of 89·04% (87·85-90·28).…”

Section: Artificial Intelligence For Diabetic Retinopathy Screening Imentioning

confidence: 95%

Artificial intelligence for diabetic retinopathy screening in Africa

Mathenge¹

2019

The Lancet Digital Health

View full text Add to dashboard Cite

Ethnicity and ocular imaging

Cited by 24 publications

References 37 publications

Measurement of size of pigmented choroidal nevus: Superiority of multicolor imaging compared to conventional color fundus photography

Measurement of size of pigmented choroidal nevus: Superiority of multicolor imaging compared to conventional color fundus photography

Retinal image quality assessment based on image clarity and content

Artificial intelligence for diabetic retinopathy screening in Africa

Contact Info

Product

Resources

About