Korean J Ophthalmol.  2017 Dec;31(6):524-532. 10.3341/kjo.2015.0143.

Retinopathy of Prematurity-assist: Novel Software for Detecting Plus Disease

• Pour EK
• Pourreza H
• Zamani KA
• Mahmoudi A
• Sadeghi AM
• Shadravan
• Karkhaneh
• Pour RR
• Esfahani M

• KMID: 2396703
• DOI: http://doi.org/10.3341/kjo.2015.0143

Abstract

PURPOSE
To 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.
METHODS
Eighty-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.
RESULTS
Thirty-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.
CONCLUSIONS
The 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.

Keyword

Retinal vessels abnormalities; Retinopathy of prematurity; Telemedicine

MeSH Terms

Diagnosis
Dilatation
Humans
Mass Screening
Neural Networks (Computer)
Politics
Retinopathy of Prematurity
Support Vector Machine
Telemedicine

Figure

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  Fig. 1 Results of algorithm, shown as vessel manifestations.

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  Fig. 2 Results of vascular meshwork extraction, with regards to exclusion of bifurcation and crossover sites.

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  Fig. 3 Schematic figure shows estimating curvature of a specific point (P) on a non-continuous curve [y = g(x)]. In order to estimate the curvature at any point, a circle is drawn with the center being on that certain point with a radius of b. With the crossover of the circle and the curve, the area between the curve and the circle can be calculated as shown in this figure, indicated by a green area A.

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