论文标题
低收入和中等收入国家的重症监护病人患者的AI授权超声心动图的机器学习案例研究
A Machine Learning Case Study for AI-empowered echocardiography of Intensive Care Unit Patients in low- and middle-income countries
论文作者
论文摘要
我们提出了一个机器学习（ML）研究案例，以说明对实时AI授权超声心动图系统的临床翻译挑战，并具有LMIC中ICU患者的数据。此类ML案例研究包括来自31例ICU患者的2D超声视频的数据准备，策展和标记，并在LMIC和模型选择中，验证和部署三个较薄的神经网络，以对顶端四腔视图进行分类。 ML启发式方法的结果表明，较薄的网络可以用有限的数据集对4CV进行分类的有希望的实施，验证和应用。我们得出结论，这项工作提到了（a）数据集，以改善人口统计学，疾病的多样性，以及（b）需要进一步研究以低成本硬件运行和实施的较薄模型，以便在LMIC中的ICU中临床翻译。可以在https://github.com/vital-ultrasound/ai-assisted-echocardiography-for-low-row-row-counce-countries上获得复制此工作的代码和其他资源。
We present a Machine Learning (ML) study case to illustrate the challenges of clinical translation for a real-time AI-empowered echocardiography system with data of ICU patients in LMICs. Such ML case study includes data preparation, curation and labelling from 2D Ultrasound videos of 31 ICU patients in LMICs and model selection, validation and deployment of three thinner neural networks to classify apical four-chamber view. Results of the ML heuristics showed the promising implementation, validation and application of thinner networks to classify 4CV with limited datasets. We conclude this work mentioning the need for (a) datasets to improve diversity of demographics, diseases, and (b) the need of further investigations of thinner models to be run and implemented in low-cost hardware to be clinically translated in the ICU in LMICs. The code and other resources to reproduce this work are available at https://github.com/vital-ultrasound/ai-assisted-echocardiography-for-low-resource-countries.
