On the improvement of schizophrenia detection with optical coherence tomography data using deep neural networks and aggregation functions
Abstract Schizophrenia is a serious mental disorder with a complex neurobiological background and a well-defined psychopathological picture. Despite many efforts, a definitive disease biomarker has still not been identified. One of the promising candidates for a disease-related biomarker could invol...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-12-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-83375-7 |
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| author | Paweł Karczmarek Małgorzata Plechawska-Wójcik Adam Kiersztyn Adam Domagała Agnieszka Wolinska Steven M. Silverstein Kamil Jonak Paweł Krukow |
| author_facet | Paweł Karczmarek Małgorzata Plechawska-Wójcik Adam Kiersztyn Adam Domagała Agnieszka Wolinska Steven M. Silverstein Kamil Jonak Paweł Krukow |
| author_sort | Paweł Karczmarek |
| collection | DOAJ |
| description | Abstract Schizophrenia is a serious mental disorder with a complex neurobiological background and a well-defined psychopathological picture. Despite many efforts, a definitive disease biomarker has still not been identified. One of the promising candidates for a disease-related biomarker could involve retinal morphology , given that the retina is a part of the central nervous system that is known to be affected in schizophrenia and related to multiple illness features. In this study Optical Coherence Tomography (OCT) data is applied to assess the different layers of the retina. OCT data were applied in the process of automatic differentiation of schizophrenic patients from healthy controls. Numerical experiments involved applying several individual 1D Convolutional Neural Network-based models as well as further using the aggregation of classification results to improve the initial classification results. The main goal of the study was to check how methods based on the aggregation of classification results work in classifying neuroanatomical features of schizophrenia. Among over 300, 000 different variants of tested aggregation operators, a few versions provided satisfactory results. |
| format | Article |
| id | doaj-art-afee1277965a4be3be62d676ffac6261 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-afee1277965a4be3be62d676ffac62612025-08-20T01:47:58ZengNature PortfolioScientific Reports2045-23222024-12-0114111410.1038/s41598-024-83375-7On the improvement of schizophrenia detection with optical coherence tomography data using deep neural networks and aggregation functionsPaweł Karczmarek0Małgorzata Plechawska-Wójcik1Adam Kiersztyn2Adam Domagała3Agnieszka Wolinska4Steven M. Silverstein5Kamil Jonak6Paweł Krukow7Department of Computational Intelligence, Lublin University of TechnologyDepartment of Computer Science, Lublin University of TechnologyDepartment of Computational Intelligence, Lublin University of TechnologyDepartment of Clinical Neuropsychiatry, Medical University of LublinDepartment of Biology and Biotechnology of Microorganisms, The John Paul II Catholic University of LublinUniversity of Rochester Medical CenterDepartment of Clinical Neuropsychiatry, Medical University of LublinDepartment of Clinical Neuropsychiatry, Medical University of LublinAbstract Schizophrenia is a serious mental disorder with a complex neurobiological background and a well-defined psychopathological picture. Despite many efforts, a definitive disease biomarker has still not been identified. One of the promising candidates for a disease-related biomarker could involve retinal morphology , given that the retina is a part of the central nervous system that is known to be affected in schizophrenia and related to multiple illness features. In this study Optical Coherence Tomography (OCT) data is applied to assess the different layers of the retina. OCT data were applied in the process of automatic differentiation of schizophrenic patients from healthy controls. Numerical experiments involved applying several individual 1D Convolutional Neural Network-based models as well as further using the aggregation of classification results to improve the initial classification results. The main goal of the study was to check how methods based on the aggregation of classification results work in classifying neuroanatomical features of schizophrenia. Among over 300, 000 different variants of tested aggregation operators, a few versions provided satisfactory results.https://doi.org/10.1038/s41598-024-83375-7 |
| spellingShingle | Paweł Karczmarek Małgorzata Plechawska-Wójcik Adam Kiersztyn Adam Domagała Agnieszka Wolinska Steven M. Silverstein Kamil Jonak Paweł Krukow On the improvement of schizophrenia detection with optical coherence tomography data using deep neural networks and aggregation functions Scientific Reports |
| title | On the improvement of schizophrenia detection with optical coherence tomography data using deep neural networks and aggregation functions |
| title_full | On the improvement of schizophrenia detection with optical coherence tomography data using deep neural networks and aggregation functions |
| title_fullStr | On the improvement of schizophrenia detection with optical coherence tomography data using deep neural networks and aggregation functions |
| title_full_unstemmed | On the improvement of schizophrenia detection with optical coherence tomography data using deep neural networks and aggregation functions |
| title_short | On the improvement of schizophrenia detection with optical coherence tomography data using deep neural networks and aggregation functions |
| title_sort | on the improvement of schizophrenia detection with optical coherence tomography data using deep neural networks and aggregation functions |
| url | https://doi.org/10.1038/s41598-024-83375-7 |
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