An optimized ensemble search approach for classification of higher-level gait disorder using brain magnetic resonance images.

IF 7 2区 医学 Q1 BIOLOGY
Computers in biology and medicine Pub Date : 2025-01-01 Epub Date: 2024-11-29 DOI:10.1016/j.compbiomed.2024.109457
Klara Mogensen, Valerio Guarrasi, Jenny Larsson, William Hansson, Anders Wåhlin, Lars-Owe Koskinen, Jan Malm, Anders Eklund, Paolo Soda, Sara Qvarlander
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引用次数: 0

Abstract

Higher-Level Gait Disorder (HLGD) is a type of gait disorder estimated to affect up to 6% of the older population. By definition, its symptoms originate from the higher-level nervous system, yet its association with brain morphology remains unclear. This study hypothesizes that there are patterns in brain morphology linked to HLGD. For the first time in the literature, this work investigates whether deep learning, in the form of convolutional neural networks, can capture patterns in magnetic resonance images to identify individuals affected by HLGD. To handle this new classification task, we propose setting up an ensemble of models. This leverages the benefits of combining classifiers instead of determining which network is the most suitable, developing a new architecture, or customizing an existing one. We introduce a computationally cost-effective search algorithm to find the optimal ensemble by leveraging a cost function of both traditional performance scores and the diversity among the models. Using a unique dataset from a large population-based cohort (VESPR), the ensemble identified by our algorithm demonstrated superior performance compared to single networks, other ensemble fusion techniques, and the best linear radiological measure. This emphasizes the importance of implementing diversity into the cost function. Furthermore, the results indicate significant morphological differences in brain structure between HLGD-affected individuals and controls, motivating research about which areas the networks base their classifications on, to get a better understanding of the pathophysiology of HLGD.

基于脑磁共振图像的高级步态障碍分类优化集成搜索方法。
高水平步态障碍(HLGD)是一种步态障碍,估计影响高达6%的老年人口。根据定义,其症状起源于高级神经系统,但其与大脑形态的关系尚不清楚。这项研究假设存在与HLGD相关的脑形态学模式。在文献中,这项工作首次研究了深度学习,以卷积神经网络的形式,是否可以捕获磁共振图像中的模式,以识别受HLGD影响的个体。为了处理这个新的分类任务,我们建议建立一个模型集合。这利用了组合分类器的好处,而不是确定哪个网络是最合适的,开发新的体系结构,或者定制现有的体系结构。我们引入了一种计算上具有成本效益的搜索算法,通过利用传统性能分数和模型之间的多样性的成本函数来找到最优集成。使用来自大型基于人群的队列(VESPR)的独特数据集,与单一网络、其他集成融合技术和最佳线性放射测量相比,我们的算法识别的集成显示出优越的性能。这强调了在成本函数中实现多样性的重要性。此外,研究结果还表明,HLGD患者和对照组在脑结构上存在显著的形态学差异,这有助于研究这些网络的分类基于哪些区域,从而更好地了解HLGD的病理生理。
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来源期刊
Computers in biology and medicine
Computers in biology and medicine 工程技术-工程:生物医学
CiteScore
11.70
自引率
10.40%
发文量
1086
审稿时长
74 days
期刊介绍: Computers in Biology and Medicine is an international forum for sharing groundbreaking advancements in the use of computers in bioscience and medicine. This journal serves as a medium for communicating essential research, instruction, ideas, and information regarding the rapidly evolving field of computer applications in these domains. By encouraging the exchange of knowledge, we aim to facilitate progress and innovation in the utilization of computers in biology and medicine.
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