pISSN: 1011-8942 eISSN: 2092-9382© 2017 The Korean Ophthalmological SocietyThis is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses /by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.524Original ArticleRetinopathy of Prematurity-assist: Novel Software for Detecting Plus DiseaseElias Khalili Pour1, Hamidreza Pourreza2, Kambiz Ameli Zamani3,Alireza Mahmoudi1, Arash Mir Mohammad Sadeghi3, Mahla Shadravan1, Reza Karkhaneh1, Ramak Rouhi Pour1, Mohammad Riazi Esfahani11Department of Vitreoretinal Surgery, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran2Department of Computer Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran3Department of Pediatric Opthalmology,Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, IranPurpose: 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 dis-ease.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 per-ceptron 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, espe-cially in centers without a skilled person in the ROP field.

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