Enhancing vessel visibility in fundus images to aid the diagnosis of retinopathy of prematurity

Ocular Pigmentation Impact on Retinal Versus Choroidal Optical Coherence Tomography Imaging in Preterm Infants

Purpose

To evaluate the association of fundus pigmentation with the visibility of retinal versus choroidal layers on optical coherence tomography (OCT) in preterm infants.

Methods

For infants enrolled in BabySTEPS, ophthalmologists recorded fundus pigmentation (blond, medium, or dark) at the first retinopathy of prematurity (ROP) examination. Bedside OCT imaging was performed at each examination, and a masked grader evaluated all OCT scans from both eyes of each infant for visibility (yes/no) of all retinal layers and of the chorio-scleral junction (CSJ). Multivariable logistic regression was used to assess associations between fundus pigmentation and visibility of all retinal layers and CSJ, controlling for potential confounders (i.e., birth weight, gestational age, sex, OCT system, pupil size, and postmenstrual age at imaging).

Results

Among 114 infants (mean birth weight, 943 grams; mean gestational age, 27.6 weeks), 43 infants (38%) had blond, 56 infants (49%) had medium, and 15 infants (13%) had dark fundus pigmentation. Of 1042 scans, all retinal layers were visible in 977 (94%) and CSJ in 895 (86%). Pigmentation was not associated with retinal layer visibility (P = 0.49), but medium and dark pigmentation were associated with decreased CSJ visibility (medium: odds ratio [OR] = 0.34, P = 0.001; dark: OR = 0.24, P = 0.009). For infants with dark pigmentation, retinal layer visibility increased (OR = 1.87 per week; P ≤ 0.001) and CSJ visibility decreased (OR = 0.78 per week; P = 0.01) with increasing age.

Conclusions

Although fundus pigmentation was not associated with the visibility of all retinal layers on OCT, darker pigmentation decreased CSJ visibility, and this effect increased with age.

Translational Relevance

The ability of bedside OCT to capture retinal layer microanatomy in preterm infants, regardless of fundus pigmentation, may represent an advantage over fundus photography for ROP telemedicine.

Global Disparities in Retinopathy of Prematurity: A Literature Review

PURPOSE

To provide an overview of the impact of retinopathy of prematurity (ROP), and the challenges in the screening, diagnosis, and treatment of ROP worldwide.

METHODS

A comprehensive search was conducted using the PubMed database from January 2011 to October 2021 using the following keywords: retinopathy of prematurity, laser, and anti-vascular endothelial growth factor (VEGF). Data on patient characteristics, ROP treatment type, and recurrence rates were collected. The countries included in these studies were classified based on 2021-2022 World Bank definitions of high, upper-middle, lower-middle, and low-income groups. Moreover, a search for surgical outcomes for ROP and screening algorithms and artificial intelligence for ROP was conducted.

RESULTS

Thirty-nine studies met the inclusion criteria. ROP treatment and outcomes showed a trend towards intravitreal anti-VEGF injections as the initial treatment for ROP globally and the treatment of recurrent ROP in high-income countries. However, laser remains the treatment of choice for ROP recurrence in middle-income countries. Surgical outcomes for ROP stage 4A, 4B and 5 are similar worldwide. The incidence of ROP and ROP-related visual impairment continue to increase globally. Although telemedicine and artificial intelligence offer potential solutions to ROP screening in resource-limited areas, the current models require further optimization to reflect the global diversity of ROP patients.

CONCLUSION

ROP screening and treatment paradigms vary widely based on country income group due to disparities in resources, limited access to care, and lack of universal guidelines.

Features extraction using encoded local binary pattern for detection and grading diabetic retinopathy

Introduction

Reliable computer diagnosis of diabetic retinopathy (DR) is needed to rescue many with diabetes who may be under threat of blindness. This research aims to detect the presence of diabetic retinopathy in fundus images and grade the disease severity without lesion segmentation.

Methods

To ensure that the fundus images are in a standard state of brightness, a series of preprocessing steps have been applied to the green channel image using histogram matching and a median filter. Then, contrast-limited adaptive histogram equalisation is performed, followed by the unsharp filter. The preprocessed image is divided into small blocks, and then each block is processed to extract uniform local binary patterns (LBPs) features. The extracted features are encoded, and the feature size is reduced to 3.5 percent of its original size. Classifiers like Support Vector Machine (SVM) and a proposed CNN model were used to classify retinal fundus images. The classification is abnormal or normal and to grade the severity of DR.

Results

Our feature extraction method was tested on a binary classifier and resulted in an accuracy of 98.37% and 98.84% on the Messidor2 and EyePACS databases, respectively. The proposed system could grade DR severity into three grades (0: no DR, 1: mild DR, and 5: moderate, severe NPDR, and PDR). It obtains an F1-score of 0.9617 and an accuracy of 95.37% on the EyePACS database, and an F1-score of 0.9860 and an accuracy of 97.57% on the Messidor2 database. The resultant values are dependent on the selection of (neighbours, radius) pairs during the extraction of LBP features.

Conclusions

This study’s results proved that the preprocessing steps are significant and had a great effect on highlighting image features. The novel method of stacking and encoding the LBP values in the feature vector greatly affects results when using SVM or CNN for classification. The proposed system outperforms the state of the artwork. The proposed CNN model performs better than SVM.

Emerging Trends and Research Foci in Artificial Intelligence for Retinal Diseases: Bibliometric and Visualization Study

BACKGROUND

Patients with retinal diseases may exhibit serious complications that cause severe visual impairment owing to a lack of awareness of retinal diseases and limited medical resources. Understanding how artificial intelligence (AI) is used to make predictions and perform relevant analyses is a very active area of research on retinal diseases. In this study, the relevant Science Citation Index (SCI) literature on the AI of retinal diseases published from 2012 to 2021 was integrated and analyzed.

OBJECTIVE

The aim of this study was to gain insights into the overall application of AI technology to the research of retinal diseases from set time and space dimensions.

METHODS

Citation data downloaded from the Web of Science Core Collection database for AI in retinal disease publications from January 1, 2012, to December 31, 2021, were considered for this analysis. Information retrieval was analyzed using the online analysis platforms of literature metrology: Bibliometrc, CiteSpace V, and VOSviewer.

RESULTS

A total of 197 institutions from 86 countries contributed to relevant publications; China had the largest number and researchers from University College London had the highest H-index. The reference clusters of SCI papers were clustered into 12 categories. "Deep learning" was the cluster with the widest range of cocited references. The burst keywords represented the research frontiers in 2018-2021, which were "eye disease" and "enhancement."

CONCLUSIONS

This study provides a systematic analysis method on the literature regarding AI in retinal diseases. Bibliometric analysis enabled obtaining results that were objective and comprehensive. In the future, high-quality retinal image-forming AI technology with strong stability and clinical applicability will continue to be encouraged.