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

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          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 25

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          Multi-class protein fold recognition using support vector machines and neural networks.

           I Dubchak,  C Ding (2001)
          Protein fold recognition is an important approach to structure discovery without relying on sequence similarity. We study this approach with new multi-class classification methods and examined many issues important for a practical recognition system. Most current discriminative methods for protein fold prediction use the one-against-others method, which has the well-known 'False Positives' problem. We investigated two new methods: the unique one-against-others and the all-against-all methods. Both improve prediction accuracy by 14-110% on a dataset containing 27 SCOP folds. We used the Support Vector Machine (SVM) and the Neural Network (NN) learning methods as base classifiers. SVMs converges fast and leads to high accuracy. When scores of multiple parameter datasets are combined, majority voting reduces noise and increases recognition accuracy. We examined many issues involved with large number of classes, including dependencies of prediction accuracy on the number of folds and on the number of representatives in a fold. Overall, recognition systems achieve 56% fold prediction accuracy on a protein test dataset, where most of the proteins have below 25% sequence identity with the proteins used in training.
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            Hierarchical committee of deep convolutional neural networks for robust facial expression recognition

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              Intelligent Energy Management Agent for a Parallel Hybrid Vehicle—Part I: System Architecture and Design of the Driving Situation Identification Process

               J.-S. Won,  R. Langari (2005)

                Author and article information

                EDP Sciences
                28 October 2019
                28 October 2019
                : 2
                : ( publisher-idID: fopen/2019/01 )
                [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

                Hellenic Army Medical Corps.

                [* ]Corresponding author: anogian@ 123456hotmail.com
                © 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.

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


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