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      Using neural networks to predict the outcome of refractive surgery for myopia

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Introduction: Refractive Surgery (RS), has advanced immensely in the last decades, utilizing methods and techniques that fulfill stringent criteria for safety, efficacy, cost-effectiveness, and predictability of the refractive outcome. Still, a non-negligible percentage of RS require corrective retreatment. In addition, surgeons should be able to advise their patients, beforehand, as to the probability that corrective RS will be necessary. The present article addresses these issues with regard to myopia and explores the use of Neural Networks as a solution to the problem of the prediction of the RS outcome.

          Methods: We used a computerized query to select patients who underwent RS with any of the available surgical techniques (PRK, LASEK, Epi-LASIK, LASIK) between January 2010 and July 2017 and we investigated 13 factors which are related to RS. The data were normalized by forcing the weights used in the forward and backward propagations to be binary; each integer was represented by a 12-bit serial code, so that following this preprocessing stage, the vector of the data values of all 13 parameters was encoded in a binary vector of 1 × (13 × 12) = 1 × 156 size. Following the preprocessing stage, eight independent Learning Vector Quantization (LVQ) networks were created in random way using the function Ivqnet of Matlab, each one of them responding to one query with (0 retreat class) or (1 correct class). The results of the eight LVQs were then averaged to permit a best estimate of the network’s performance while a voting procedure by the neural nets was used to arrive at the outcome

          Results: Our algorithm was able to predict in a statistically significant way (as evidenced by Cohen’s Kappa test result of 0.7595) the need for retreatment after initial RS with good sensitivity (0.8756) and specificity (0.9286).

          Conclusion: The results permit us to be optimistic about the future of using neural networks for the prediction of the outcome and, eventually, the planning of RS.

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          Most cited references 32

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          Understanding interobserver agreement: the kappa statistic.

          Items such as physical exam findings, radiographic interpretations, or other diagnostic tests often rely on some degree of subjective interpretation by observers. Studies that measure the agreement between two or more observers should include a statistic that takes into account the fact that observers will sometimes agree or disagree simply by chance. The kappa statistic (or kappa coefficient) is the most commonly used statistic for this purpose. A kappa of 1 indicates perfect agreement, whereas a kappa of 0 indicates agreement equivalent to chance. A limitation of kappa is that it is affected by the prevalence of the finding under observation. Methods to overcome this limitation have been described.
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            The kappa statistic in reliability studies: use, interpretation, and sample size requirements.

            This article examines and illustrates the use and interpretation of the kappa statistic in musculoskeletal research. The reliability of clinicians' ratings is an important consideration in areas such as diagnosis and the interpretation of examination findings. Often, these ratings lie on a nominal or an ordinal scale. For such data, the kappa coefficient is an appropriate measure of reliability. Kappa is defined, in both weighted and unweighted forms, and its use is illustrated with examples from musculoskeletal research. Factors that can influence the magnitude of kappa (prevalence, bias, and non-independent ratings) are discussed, and ways of evaluating the magnitude of an obtained kappa are considered. The issue of statistical testing of kappa is considered, including the use of confidence intervals, and appropriate sample sizes for reliability studies using kappa are tabulated. The article concludes with recommendations for the use and interpretation of kappa.
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              Clinically applicable deep learning for diagnosis and referral in retinal disease

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                Author and article information

                Affiliations
                [1 ] Ophthalmica Institute of Ophthalmology and Microsurgery, , V. Olgas 196, Thessaloniki 546 55, Greece,
                [2 ] Faculty of Medicine, Aristotle University of Thessaloniki, , Thessaloniki 54124, Greece,
                [3 ] Laboratory of Information Technologies, Faculty of Information Science and Informatics, Ionian University, , Corfu 49100, Greece,
                [4 ] Department of Ophthalmology, Cornea, Cataract and Refractive Surgery, University Eye Hospital Basel USB, , Mittlere Strasse 91, 4031 Basel, Switzerland,
                [5 ] Association for Training in Biomedical Technology, , 6 Aristogeitonos Street, Thessaloniki 54628, Greece,
                Author notes
                [a]

                Hellenic Army Medical Corps.

                [* ]Corresponding author: anogian@ 123456hotmail.com
                Journal
                fopen
                https://www.4open-sciences.org
                4open
                4open
                EDP Sciences
                2557-0250
                28 October 2019
                28 October 2019
                2019
                : 2
                : ( publisher-idID: fopen/2019/01 )
                fopen190018
                10.1051/fopen/2019024
                © M. Balidis et al., Published by EDP Sciences, 2019

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                Counts
                Figures: 2, Tables: 8, Equations: 22, References: 35, Pages: 13
                Product
                Self URI (journal page): https://www.4open-sciences.org/
                Categories
                Life Sciences - Medicine
                Research Article
                Custom metadata
                4open 2019, 2, 29
                2019
                2019
                2019

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