Quantifying Parkinson’s Disease Motor Severity Under Uncertainty Using MDS-UPDRS Videos
推荐理由
本文提出评分混淆矩阵解决不同评分者对同一帕金森病患者的MDS-UPDRS视频有不同评分导致的标签噪声问题，并设计了神经网络模型对输入的MDS-UPDRS视频预测分类其MDS-UPDRS评分，是计算机视觉技术在跟踪评估帕金森患者病重程度方向的实际应用示例。
| 文章简介 | |
|---|---|
| 期刊 | Medical Image Analysis |
| 发表年份 | 2021 |
| DOI | 10.1016/j.media.2021.102179 |
| 类型 | 研究性工作 |
| 领域 | 计算机视觉 |
| 引用量 | 44 |
| 推荐信息 | |
|---|---|
| 推荐人 | 阿璐思 |
| 审核 | 王伟明 张琛 |
| 推荐小组 | 动捕小组 |
摘要
Parkinson’s disease (PD) is a brain disorder that primarily affects motor function, leading to slow movement, tremor, and stiffness, as well as postural instability and difficulty with walking/balance. The severity of PD motor impairments is clinically assessed by part III of the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS), a universally-accepted rating scale. However, experts often disagree on the exact scoring of individuals. In the presence of label noise, training a machine learning model using only scores from a single rater may introduce bias, while training models with multiple noisy ratings is a challenging task due to the inter-rater variabilities. In this paper, we introduce an ordinal focal neural network to estimate the MDS-UPDRS scores from input videos, to leverage the ordinal nature of MDS-UPDRS scores and combat class imbalance. To handle multiple noisy labels per exam, the training of the network is regularized via rater confusion estimation (RCE), which encodes the rating habits and skills of raters via a confusion matrix. We apply our pipeline to estimate MDS-UPDRS test scores from their video recordings including gait (with multiple Raters, R = 3) and finger tapping scores (single rater). On a sizable clinical dataset for the gait test (N = 55), we obtained a classification accuracy of 72% with majority vote as ground-truth, and an accuracy of ∼84% of our model predicting at least one of the raters’ scores. Our work demonstrates how computer-assisted technologies can be used to track patients and their motor impairments, even when there is uncertainty in the clinical ratings. The latest version of the code will be available at https://github.com/mlu355/PD-Motor-Severity-Estimation.
细分领域
< | 基于单目视频 | 帕金森患者UPDRS评分预测