Karczmarek Paweł, Plechawska-Wójcik Małgorzata, Kiersztyn Adam, Domagała Adam, Wolinska Agnieszka, Silverstein Steven M, Jonak Kamil, Krukow Paweł
Department of Computational Intelligence, Lublin University of Technology, ul. Nadbystrzycka 38B, 20-618, Lublin, Poland.
Department of Computer Science, Lublin University of Technology, ul. Nadbystrzycka 36B, 20-618, Lublin, Poland.
Sci Rep. 2024 Dec 30;14(1):31903. doi: 10.1038/s41598-024-83375-7.
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.
精神分裂症是一种严重的精神障碍，具有复杂的神经生物学背景和明确的精神病理学表现。尽管付出了诸多努力，但仍未确定一种明确的疾病生物标志物。鉴于视网膜是中枢神经系统的一部分，已知在精神分裂症中会受到影响且与多种疾病特征相关，因此与疾病相关的生物标志物的一个有前景的候选者可能涉及视网膜形态。在本研究中，光学相干断层扫描（OCT）数据被用于评估视网膜的不同层。OCT数据被应用于从健康对照中自动区分精神分裂症患者的过程。数值实验涉及应用几个基于一维卷积神经网络的个体模型，以及进一步使用分类结果的聚合来改善初始分类结果。该研究的主要目标是检验基于分类结果聚合的方法在对精神分裂症的神经解剖特征进行分类时的效果。在超过300,000种不同的测试聚合算子变体中，有几个版本提供了令人满意的结果。
