机器学习增强型电阻抗肌电图诊断和跟踪脊髓性肌肉萎缩症的进展。

IF 2.3 4区 医学 Q3 BIOPHYSICS
Buket Sonbas Cobb, Stephen J Kolb, Seward B Rutkove
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引用次数: 0

摘要

目的: 评估电阻抗肌电图(EIM)与机器学习相结合检测小儿脊髓性肌萎缩症(SMA)和疾病进展的效果 方法: 作为 NeuroNEXT SMA 生物标记物研究的一部分,我们对 26 名 SMA 婴儿和 27 名健康婴儿进行了登记和 EIM 评估。我们对这些数据采用了多种现代、有监督的机器学习方法,首先寻求区分健康和 SMA 肌肉,然后使用最佳方法跟踪 SMA 的进展。这与当时使用单频或多频评估方法达到的最高 66% 的准确率形成了鲜明对比。尽管基于多频反应的测量方法在这方面也表现出色,但 LDA 分数也能有效跟踪病情进展。这里确定的标准值和趋势对该技术的其他儿科应用也很有价值。这里应用的基本分析方法也可能适用于其他以肌肉萎缩为特征的神经肌肉疾病。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine learning-enhanced electrical impedance myography to diagnose and track spinal muscular atrophy progression.

Objective.To evaluate electrical impedance myography (EIM) in conjunction with machine learning (ML) to detect infantile spinal muscular atrophy (SMA) and disease progression.Approach. Twenty-six infants with SMA and twenty-seven healthy infants had been enrolled and assessed with EIM as part of the NeuroNEXT SMA biomarker study. We applied a variety of modern, supervised ML approaches to this data, first seeking to differentiate healthy from SMA muscle, and then, using the best method, to track SMA progression.Main Results.Several of the ML algorithms worked well, but linear discriminant analysis (LDA) achieved 88.6% accuracy on subject muscles studied. This contrasts with a maximum of 60% accuracy that could be achieved using the single or multifrequency assessment approaches available at the time. LDA scores were also able to track progression effectively, although a multifrequency reactance-based measure also performed very well in this context.Significance.EIM enhanced with ML promises to be effective for providing effective diagnosis and tracking children and adults with SMA treated with currently available therapies. The normative trends identified here may also inform future applications of the technology in very young children. The basic analyses applied here could also likely be applied to other neuromuscular disorders characterized by muscle atrophy.

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来源期刊
Physiological measurement
Physiological measurement 生物-工程:生物医学
CiteScore
5.50
自引率
9.40%
发文量
124
审稿时长
3 months
期刊介绍: Physiological Measurement publishes papers about the quantitative assessment and visualization of physiological function in clinical research and practice, with an emphasis on the development of new methods of measurement and their validation. Papers are published on topics including: applied physiology in illness and health electrical bioimpedance, optical and acoustic measurement techniques advanced methods of time series and other data analysis biomedical and clinical engineering in-patient and ambulatory monitoring point-of-care technologies novel clinical measurements of cardiovascular, neurological, and musculoskeletal systems. measurements in molecular, cellular and organ physiology and electrophysiology physiological modeling and simulation novel biomedical sensors, instruments, devices and systems measurement standards and guidelines.
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