Ocular Sequelae in Premature Infants

Page, Jacqueline M.; Schneeweiss, Suzan; Whyte, Hilary; Harvey, Patricia

doi:10.1542/peds.92.6.787

Cited by 69 publications

(4 citation statements)

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“…It stands as the primary preventable cause of childhood blindness globally [4,5], categorized into five stages, ranging from mild to severe [6]. Accurate ROP detection in premature infants is critical to prevent irreversible vision loss, avoiding complications like astigmatism, myopia, glaucoma, cataracts, anisometropia, amblyopia, strabismus, and retinal detachment [7,8].…”

Section: Introductionmentioning

confidence: 99%

Evaluating color efficacy in fundus photography for artificial intelligence classification of retinopathy of prematurity

Ebrahimi,

Le,

Abtahi

et al. 2024

Ophthalmic Technologies XXXIV

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This study investigates the efficacy of the red, green, and blue channels in color fundus photography on the deep learning classification of retinopathy of prematurity (ROP). We used a total of 200 color fundus images from four ROP stages and applied the transfer learning for deep learning classification. To enhance visibility, contrast limiting adaptive histogram equalization (CLAHE) was utilized. Multi-color-channel fusion approach was tested to determine its effect on ROP classification. For individual channel classification, the green channel demonstrated the best results, with an accuracy of 80.5%, sensitivity of 61%, and specificity of 87%. Multi-color-channel fusion provided slightly better performance than green channel with an accuracy of 81%, sensitivity of 62%, and specificity of 87.33%. After CLAHE, the red-only, green-only, and RGB-fusion showed comparable performance, with accuracies of 83.5%, 84%, and 84.25, sensitivities of 67%, 68% and 68.5%, and specificities of 89%, 89.33% and 89.50%, respectively. This observation suggests that the red channel after contrast enhancement can provide sufficient information for ROP stage classification.

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Section: Introductionmentioning

confidence: 99%

Evaluating color efficacy in fundus photography for artificial intelligence classification of retinopathy of prematurity

Ebrahimi,

Le,

Abtahi

et al. 2024

Ophthalmic Technologies XXXIV

View full text Add to dashboard Cite

show abstract

“…The severity of ROP is classified into five stages, corresponding to mild to severe stages 6 . Appropriate detection and accurate ROP diagnosis in premature infants is crucial to prevent irreversible vision loss and associated visual developmental complications such as astigmatism, myopia, glaucoma, cataracts, anisometropia, amblyopia, strabismus, and retinal detachment 7 , 8 …”

Section: Introductionmentioning

confidence: 99%

“…6 Appropriate detection and accurate ROP diagnosis in premature infants is crucial to prevent irreversible vision loss and associated visual developmental complications such as astigmatism, myopia, glaucoma, cataracts, anisometropia, amblyopia, strabismus, and retinal detachment. 7,8 Color fundus photography is widely used for ROP screening and diagnosis. 9 It involves white light illumination for color fundus imaging.…”

Section: Introductionmentioning

confidence: 99%

Assessing spectral effectiveness in color fundus photography for deep learning classification of retinopathy of prematurity

Ebrahimi,

Le,

Abtahi

et al. 2024

J. Biomed. Opt.

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Retinopathy of prematurity (ROP) poses a significant global threat to childhood vision, necessitating effective screening strategies. This study addresses the impact of color channels in fundus imaging on ROP diagnosis, emphasizing the efficacy and safety of utilizing longer wavelengths, such as red or green for enhanced depth information and improved diagnostic capabilities.Aim: This study aims to assess the spectral effectiveness in color fundus photography for the deep learning classification of ROP.Approach: A convolutional neural network end-to-end classifier was utilized for deep learning classification of normal, stage 1, stage 2, and stage 3 ROP fundus images. The classification performances with individual-color-channel inputs, i.e., red, green, and blue, and multi-color-channel fusion architectures, including early-fusion, intermediate-fusion, and late-fusion, were quantitatively compared.Results: For individual-color-channel inputs, similar performance was observed for green channel (88.00% accuracy, 76.00% sensitivity, and 92.00% specificity) and red channel (87.25% accuracy, 74.50% sensitivity, and 91.50% specificity), which is substantially outperforming the blue channel (78.25% accuracy, 56.50% sensitivity, and 85.50% specificity). For multi-color-channel fusion options, the early-fusion and intermediate-fusion architecture showed almost the same performance when compared to the green/red channel input, and they outperformed the late-fusion architecture.Conclusions: This study reveals that the classification of ROP stages can be effectively achieved using either the green or red image alone. This finding enables the exclusion of blue images, acknowledged for their increased susceptibility to light toxicity.

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“…The severity of ROP is classified into five stages, corresponding to mild to severe stages [6]. Appropriate detection and accurate ROP diagnosis in premature infants is crucial to prevent irreversible vision loss and associated visual developmental complications such as astigmatism, myopia, glaucoma, cataracts, anisometropia, amblyopia, strabismus, and retinal detachment [7,8].…”

Section: Introductionmentioning

confidence: 99%

Assessing Spectral Effectiveness in Color Fundus Photography for Deep Learning Classification of Retinopathy of Prematurity

Ebrahimi,

Le,

Abtahi

et al. 2023

Preprint

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The purpose of this study is to investigate spectral effectiveness, i.e., the effects of color channels, in fundus photography on deep learning classification of retinopathy of prematurity (ROP). A convolutional neural network (CNN) end-to-end classifier was utilized for deep learning classification of normal, stage 1, stage 2, and stage 3 ROP fundus images. The classification performances with individual-color-channel inputs, i.e., red, green, and blue, and multi-color-channel fusion architectures, including early-fusion, intermediate-fusion, and late-fusion, were quantitatively compared. For individual-color-channel inputs, similar performance was observed for green channel (88.00% accuracy, 76.00% sensitivity, and 92.00% specificity) and red channel (87.25% accuracy, 74.50% sensitivity, and 91.50% specificity), which is substantially outperforming the blue channel (78.25% accuracy, 56.50% sensitivity, and 85.50% specificity). For multi-color-channel fusion options, the early-fusion and intermediate-fusion architecture showed almost the same performance when compared to the green/red channel input, and they outperformed the late-fusion architecture. To interpret the deep learning performance, the Gradient-weighted Class Activation Mapping (Grad-CAM) was utilized to identify spatial characteristics for ROP classification. Comparative analysis indicates that the green or red channel can provide sufficient information for ROP stage classification, without the involvement of blue images.

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Ocular Sequelae in Premature Infants

Cited by 69 publications

References 0 publications

Evaluating color efficacy in fundus photography for artificial intelligence classification of retinopathy of prematurity

Evaluating color efficacy in fundus photography for artificial intelligence classification of retinopathy of prematurity

Assessing spectral effectiveness in color fundus photography for deep learning classification of retinopathy of prematurity

Assessing Spectral Effectiveness in Color Fundus Photography for Deep Learning Classification of Retinopathy of Prematurity

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