ArticleKorean journal of ophthalmology : KJO2017
Retinopathy of Prematurity-assist: Novel Software for Detecting Plus Disease.
Article in Korean journal of ophthalmology : KJO, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A Method for Peripapillary Retinal Vessel Tortuosity Measurement and Its Clinical Correspondence.Translational vision science & technology · 2025Article
- Automated diagnosis of plus disease in retinopathy of prematurity using quantification of vessels characteristics.Scientific reports · 2024Article
- Fairness and generalisability in deep learning of retinopathy of prematurity screening algorithms: a literature review.BMJ open ophthalmology · 2023Review
- GabROP: Gabor Wavelets-Based CAD for Retinopathy of Prematurity Diagnosis via Convolutional Neural Networks.Diagnostics (Basel, Switzerland) · 2023Article
- DIAROP: Automated Deep Learning-Based Diagnostic Tool for Retinopathy of Prematurity.Diagnostics (Basel, Switzerland) · 2021Article
- Plus Disease in Retinopathy of Prematurity: Convolutional Neural Network Performance Using a Combined Neural Network and Feature Extraction Approach.Translational vision science & technology · 2020Article
- Smartphone-based fundus photography for screening of plus-disease retinopathy of prematurity.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2019Article
- Curious case of retrolental mass: An unusual tele-screening artifact.Indian journal of ophthalmology · 2019Article
- A narrative review of retinal vascular parameters and the applications (Part II): Diagnosis in stroke.Brain circulationReview
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9 authors.
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Abstract
purposeTo design software with a novel algorithm, which analyzes the tortuosity and vascular dilatation in fundal images of retinopathy of prematurity (ROP) patients with an acceptable accuracy for detecting plus disease.
methodsEighty-seven well-focused fundal images taken with RetCam were classified to three groups of plus, non-plus, and pre-plus by agreement between three ROP experts. Automated algorithms in this study were designed based on two methods: the curvature measure and distance transform for assessment of tortuosity and vascular dilatation, respectively as two major parameters of plus disease detection.
resultsThirty-eight plus, 12 pre-plus, and 37 non-plus images, which were classified by three experts, were tested by an automated algorithm and software evaluated the correct grouping of images in comparison to expert voting with three different classifiers, k-nearest neighbor, support vector machine and multilayer perceptron network. The plus, pre-plus, and non-plus images were analyzed with 72.3%, 83.7%, and 84.4% accuracy, respectively.
conclusionsThe new automated algorithm used in this pilot scheme for diagnosis and screening of patients with plus ROP has acceptable accuracy. With more improvements, it may become particularly useful, especially in centers without a skilled person in the ROP field.
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