Department of Neurology at Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States of America.
Department of Electrical and Electronic Engineering, Harran University, Sanliurfa, Turkey.
Physiol Meas. 2024 Sep 6;45(9). doi: 10.1088/1361-6579/ad74d5.
To evaluate electrical impedance myography (EIM) in conjunction with machine learning (ML) to detect infantile spinal muscular atrophy (SMA) and disease progression.. 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.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.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.
评估电刺激阻抗肌图（EIM）与机器学习（ML）相结合，以检测婴儿脊髓性肌萎缩症（SMA）和疾病进展。NeuroNEXT SMA 生物标志物研究纳入了 26 名 SMA 婴儿和 27 名健康婴儿，并进行了 EIM 评估。我们将各种现代监督 ML 方法应用于这些数据，首先是区分健康肌肉和 SMA 肌肉，然后使用最佳方法来跟踪 SMA 进展。几种 ML 算法表现良好，但线性判别分析（LDA）在研究的肌肉上达到了 88.6%的准确率。这与当时可用的单一或多频评估方法所能达到的最高 60%的准确率形成对比。LDA 评分也能够有效地跟踪进展，尽管在这种情况下基于多频电抗的测量也表现得非常好。增强了 ML 的 EIM 有望有效地为目前可用疗法治疗的 SMA 儿童和成人提供有效诊断和跟踪。这里确定的正常趋势也可能为该技术在非常年幼的儿童中的未来应用提供信息。这里应用的基本分析也可能适用于其他以肌肉萎缩为特征的神经肌肉疾病。
