使用可解释的机器学习方法对中风诊断中的生物力学时间序列进行创新的可视化方法:一项概念验证研究

IF 2.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Information (Switzerland) Pub Date : 2023-10-12 DOI:10.3390/info14100559
Kyriakos Apostolidis, Christos Kokkotis, Evangelos Karakasis, Evangeli Karampina, Serafeim Moustakidis, Dimitrios Menychtas, Georgios Giarmatzis, Dimitrios Tsiptsios, Konstantinos Vadikolias, Nikolaos Aggelousis
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引用次数: 0

摘要

中风仍然是世界范围内死亡和残疾的主要原因。通过生物力学时间序列数据与人工智能(AI)相结合来诊断中风的努力面临着巨大的挑战,特别是在参与者数量有限的情况下。当处理小数据集时,挑战会升级,这是初步医学研究中的常见情况。虽然最近的进展已经带来了擅长处理稀疏数据的少量学习算法,但本文开创了一种独特的方法,涉及以可视化为中心的方法,以导航基于步态分析衍生的生物力学数据诊断中风幸存者的小数据挑战。采用连体神经网络(snn),我们的方法将生物力学时间序列转换为视觉直观的图像,促进了独特的分析镜头。封装的运动学数据包括一系列步态指标,包括足瘫和非足瘫腿的踝关节、膝关节、髋关节和质心的三维运动。在视觉转换之后,SNN作为一个有效的特征提取器,将数据映射到一个有利于分类的高维特征空间。提取的特征随后被输入到各种机器学习(ML)模型中,如支持向量机(svm)、随机森林(RF)或神经网络(NN)进行分类。为了追求更高的可解释性,这是医疗人工智能应用的基石,我们采用了Grad-CAM(类别激活图)工具,以视觉方式突出显示影响模型决策的关键区域。我们的方法虽然是探索性的,但展示了利用可视化生物力学数据进行中风诊断的有前途的途径,在我们的初步数据集中实现了完美的分类率。对生成图像的视觉检查阐明了清晰的类别分离(100%),强调了这种可视化驱动方法在小数据领域的潜力。这项概念验证研究强调了视觉数据转换在使用有限数据提高脑卒中诊断的可解释性和性能方面的新颖性,为未来更大规模评估的研究奠定了坚实的基础。
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Innovative Visualization Approach for Biomechanical Time Series in Stroke Diagnosis Using Explainable Machine Learning Methods: A Proof-of-Concept Study
Stroke remains a predominant cause of mortality and disability worldwide. The endeavor to diagnose stroke through biomechanical time-series data coupled with Artificial Intelligence (AI) poses a formidable challenge, especially amidst constrained participant numbers. The challenge escalates when dealing with small datasets, a common scenario in preliminary medical research. While recent advances have ushered in few-shot learning algorithms adept at handling sparse data, this paper pioneers a distinctive methodology involving a visualization-centric approach to navigating the small-data challenge in diagnosing stroke survivors based on gait-analysis-derived biomechanical data. Employing Siamese neural networks (SNNs), our method transforms a biomechanical time series into visually intuitive images, facilitating a unique analytical lens. The kinematic data encapsulated comprise a spectrum of gait metrics, including movements of the ankle, knee, hip, and center of mass in three dimensions for both paretic and non-paretic legs. Following the visual transformation, the SNN serves as a potent feature extractor, mapping the data into a high-dimensional feature space conducive to classification. The extracted features are subsequently fed into various machine learning (ML) models like support vector machines (SVMs), Random Forest (RF), or neural networks (NN) for classification. In pursuit of heightened interpretability, a cornerstone in medical AI applications, we employ the Grad-CAM (Class Activation Map) tool to visually highlight the critical regions influencing the model’s decision. Our methodology, though exploratory, showcases a promising avenue for leveraging visualized biomechanical data in stroke diagnosis, achieving a perfect classification rate in our preliminary dataset. The visual inspection of generated images elucidates a clear separation of classes (100%), underscoring the potential of this visualization-driven approach in the realm of small data. This proof-of-concept study accentuates the novelty of visual data transformation in enhancing both interpretability and performance in stroke diagnosis using limited data, laying a robust foundation for future research in larger-scale evaluations.
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来源期刊
Information (Switzerland)
Information (Switzerland) Computer Science-Information Systems
CiteScore
6.90
自引率
0.00%
发文量
515
审稿时长
11 weeks
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