机器学习在儿童胸腹运动不同步自动识别中的应用。
Machine learning for automatic identification of thoracoabdominal asynchrony in children.
机构信息
Biomedical Research, Nemours/Alfred I. duPont Hospital for Children, Wilmington, DE, USA.
Division of Pulmonary Medicine, Nemours/Alfred I. duPont Hospital for Children, Wilmington, DE, USA.
出版信息
Pediatr Res. 2021 Apr;89(5):1232-1238. doi: 10.1038/s41390-020-1032-1. Epub 2020 Jul 3.
BACKGROUND
The current methods for assessment of thoracoabdominal asynchrony (TAA) require offline analysis on the part of physicians (respiratory inductance plethysmography (RIP)) or require experts for interpretation of the data (sleep apnea detection).
METHODS
To assess synchrony between the thorax and abdomen, the movements of the two compartments during quiet breathing were measured using pneuRIP. Fifty-one recordings were obtained: 20 were used to train a machine-learning (ML) model with elastic-net regularization, and 31 were used to test the model's performance. Two feature sets were explored: (1) phase difference (ɸ) between the thoracic and abdominal signals and (2) inverse cumulative percentage (ICP), which is an alternate measure of data distribution. To compute accuracy of training, the model outcomes were compared with five experts' assessments.
RESULTS
Accuracies of 61.3% and 90.3% were obtained using ɸ and ICP features, respectively. The inter-rater reliability (i.r.r.) of the assessments of experts was 0.402 and 0.684 when they used ɸ and ICP to identify TAA, respectively.
CONCLUSIONS
With this pilot study, we show the efficacy of the ICP feature and ML in developing an accurate automated approach to identifying TAA that reduces time and effort for diagnosis. ICP also helped improve consensus among experts.
IMPACT
Our article presents an automated approach to identifying thoracic abdominal asynchrony using machine learning and the pneuRIP device. It also shows how a modified statistical measure of cumulative frequency can be used to visualize the progression of the pulmonary functionality along time. The pulmonary testing method we developed gives patients and doctors a noninvasive and easy to administer and diagnose approach. It can be administered remotely, and alerts can be transmitted to the physician. Further, the test can also be used to monitor and assess pulmonary function continuously for prolonged periods, if needed.
背景
目前评估胸腹不同步（TAA）的方法需要医生进行离线分析（呼吸感应体积描记法（RIP））或需要专家来解释数据（睡眠呼吸暂停检测）。
方法
为了评估胸部和腹部之间的同步性，使用 pneuRIP 测量了两个隔室在安静呼吸时的运动。获得了 51 次记录：其中 20 次用于训练具有弹性网正则化的机器学习（ML）模型，31 次用于测试模型的性能。探索了两个特征集：（1）胸腹部信号之间的相位差（ɸ）和（2）逆累积百分比（ICP），ICP 是数据分布的替代度量。为了计算训练的准确性，将模型的结果与五位专家的评估进行了比较。
结果
使用ɸ和 ICP 特征分别获得了 61.3%和 90.3%的准确性。当专家使用ɸ和 ICP 来识别 TAA 时，他们的评估者间可靠性（i.r.r.）分别为 0.402 和 0.684。
结论
通过这项初步研究，我们展示了 ICP 特征和 ML 在开发一种准确的自动化方法识别 TAA 方面的功效，该方法可以减少诊断的时间和精力。ICP 还帮助提高了专家之间的共识。
影响
我们的文章提出了一种使用机器学习和 pneuRIP 设备识别胸腹不同步的自动化方法。它还展示了如何使用累积频率的修改统计度量来可视化随着时间的推移肺部功能的进展。我们开发的肺部测试方法为患者和医生提供了一种非侵入性、易于管理和诊断的方法。它可以远程进行，并且可以向医生发送警报。此外，如果需要，该测试还可以用于长时间连续监测和评估肺部功能。
Zotero 插件