Comparison of expert graders to computer-assisted image analysis of the retina in retinopathy of prematurity

Shah, Deepika N.; Wilson, Clare; Ying, Gui‐Shuang; Karp, Karen A.; Cocker, Kenneth D.; Ng, Jeffrey; Schulenburg, Ed; Fielder, Alistair R.; Mills, Monte D.; Quinn, Graham E.

doi:10.1136/bjo.2010.185363

Cited by 19 publications

(11 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Despite widely accepted guidelines and diagnostic criteria for identification of plus disease, large interobserver differences exist, 137 although this situation might be improved with computer-based image analysis of the retina. 130,138–141 However, vessel calibre might be changed by non-adverted compression by the camera lens. 142 …”

Section: Treatmentmentioning

confidence: 99%

Retinopathy of prematurity

2013

View full text Add to dashboard Cite

The immature retinas of preterm neonates are susceptible to insults that disrupt neurovascular growth, leading to retinopathy of prematurity. Suppression of growth factors due to hyperoxia and loss of the maternal–fetal interaction result in an arrest of retinal vascularisation (phase 1). Subsequently, the increasingly metabolically active, yet poorly vascularised, retina becomes hypoxic, stimulating growth factor-induced vasoproliferation (phase 2), which can cause retinal detachment. In very premature infants, controlled oxygen administration reduces but does not eliminate retinopathy of prematurity. Identification and control of factors that contribute to development of retinopathy of prematurity is essential to prevent progression to severe sight-threatening disease and to limit comorbidities with which the disease shares modifiable risk factors. Strategies to prevent retinopathy of prematurity will depend on optimisation of oxygen saturation, nutrition, and normalisation of concentrations of essential factors such as insulin-like growth factor 1 and ω-3 polyunsaturated fatty acids, as well as curbing of the effects of infection and inflammation to promote normal growth and limit suppression of neurovascular development.

show abstract

Section: Treatmentmentioning

confidence: 99%

Retinopathy of prematurity

2013

View full text Add to dashboard Cite

show abstract

“…Images were analyzed with Computer-Aided Image Analysis of the Retina (CAIAR), a semiautomated digital image vessel analysis tool for quantitative analysis of retinal photos that identifies retinal vessels and measures width in pixels and tortuosity in arbitrary units and has demonstrated good expert correlation and predictive ability for discriminating severe ROP. 8,10,11 Each NM200D image was analyzed to 350 pixels from the center of the optic disk, or about 1.5 disk diameters. Two graders (JYK, DHG) who were masked to patient identity, imaging time point, and ROP diagnosis were randomly assigned images from among a total of 77 images from any patient and any photographic session.…”

Section: Image Analysismentioning

confidence: 99%

Retinal vessel changes after laser treatment for retinopathy of prematurity

Kwon

Ghodasra

Karp

et al. 2012

Journal of American Association for Pediatric Ophthalmology and

View full text Add to dashboard Cite

“…15 Collaborative work using CAIAR to analyze greater-resolution Nidek images has shown width values are significantly different in infants without versus with ROP. 20 This study adds to current knowledge that when arteriolar and venular quantifications are combined for analysis, either in a 4-or 8-vessel paradigm, the A1V tortuosity, but not width, increases significantly with ROP presence and severity. Although this finding may not be of fundamental clinical interest, it does have implications for clinical research and future practice.…”

Section: Discussionmentioning

confidence: 69%