用卷积神经网络从SPECT图像中早期精准识别帕金森病
Accurate early detection of Parkinson's disease from SPECT imaging through Convolutional Neural Networks
- 基于SPECT图像特征训练卷积神经网络进行诊断
- 模型对早期帕金森病和SWEDD人群识别准确率高
- 可辅助临床医生减少误诊，尤其适合神经科医师参考
帕金森病（PD）的早期精准检测是极具临床意义的诊断挑战。例如，部分被临床诊断为PD的患者（称为SWEDD）其SPECT扫描结果正常，但随访数年后被重新归类为非PD，而在此期间已接受帕金森药物治疗，反而造成伤害。本文利用SPECT图像特征构建机器学习模型，用于早期识别PD及SWEDD人群。实验结果显示，这些模型具备高精度，具有辅助临床诊断的潜力。
原文摘要 · Abstract (English)
Early and accurate detection of Parkinson's disease (PD) is a crucial diagnostic challenge carrying immense clinical significance, for effective treatment regimens and patient management. For instance, a group of subjects termed SWEDD who are clinically diagnosed as PD, but show normal Single Photon Emission Computed Tomography (SPECT) scans, change their diagnosis as non-PD after few years of follow up, and in the meantime, they are treated with PD medications which do more harm than good. In this work, machine learning models are developed using features from SPECT images to detect early PD and SWEDD subjects from normal. These models were observed to perform with high accuracy. It is inferred from the study that these diagnostic models carry potential to help PD clinicians in the diagnostic process
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