School of Computer and Systems Sciences, Jawaharlal Nehru University, New Mehrauli Road, New Delhi-110067, India.
Department of NMR, All India Institute of Medical Sciences, Ansari Nagar, New Delhi-110029, India.
Cereb Cortex. 2024 Apr 1;34(4). doi: 10.1093/cercor/bhae132.
Spinocerebellar ataxia type 12 is a hereditary and neurodegenerative illness commonly found in India. However, there is no established noninvasive automatic diagnostic system for its diagnosis and identification of imaging biomarkers. This work proposes a novel four-phase machine learning-based diagnostic framework to find spinocerebellar ataxia type 12 disease-specific atrophic-brain regions and distinguish spinocerebellar ataxia type 12 from healthy using a real structural magnetic resonance imaging dataset. Firstly, each brain region is represented in terms of statistics of coefficients obtained using 3D-discrete wavelet transform. Secondly, a set of relevant regions are selected using a graph network-based method. Thirdly, a kernel support vector machine is used to capture nonlinear relationships among the voxels of a brain region. Finally, the linear relationship among the brain regions is captured to build a decision model to distinguish spinocerebellar ataxia type 12 from healthy by using the regularized logistic regression method. A classification accuracy of 95% and a harmonic mean of precision and recall, i.e. F1-score of 94.92%, is achieved. The proposed framework provides relevant regions responsible for the atrophy. The importance of each region is captured using Shapley Additive exPlanations values. We also performed a statistical analysis to find volumetric changes in spinocerebellar ataxia type 12 group compared to healthy. The promising result of the proposed framework shows that clinicians can use it for early and timely diagnosis of spinocerebellar ataxia type 12.
脊髓小脑性共济失调 12 型是一种常见于印度的遗传性和神经退行性疾病。然而，目前还没有建立用于诊断和识别成像生物标志物的非侵入性自动诊断系统。本工作提出了一种新颖的基于四阶段机器学习的诊断框架，使用真实的结构磁共振成像数据集来寻找脊髓小脑性共济失调 12 型特有的萎缩脑区，并将其与健康个体区分开来。首先，使用 3D 离散小波变换获取的系数统计来表示每个脑区。其次，使用基于图网络的方法选择一组相关区域。然后，使用核支持向量机来捕获脑区体素之间的非线性关系。最后，使用正则化逻辑回归方法，通过捕获脑区之间的线性关系来构建一个决策模型，以区分脊髓小脑性共济失调 12 型和健康个体。该方法实现了 95%的分类准确率和 94.92%的调和平均精度和召回率（即 F1 得分）。所提出的框架提供了负责萎缩的相关区域。使用 Shapley Additive exPlanations 值捕获每个区域的重要性。我们还进行了统计分析，以发现脊髓小脑性共济失调 12 型组与健康个体相比的体积变化。该框架的有前途的结果表明，临床医生可以使用它进行脊髓小脑性共济失调 12 型的早期和及时诊断。
