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.

在这项工作中，我们介绍了DCGAN的实证研究，包括超参数启发式方法和图像质量评估，以解决研究数据集的稀缺性，以研究胎儿头超声。我们提出了实验，以显示不同图像分辨率，时期，数据集大小输入和对四个指标质量图像评估的学习速率的影响：互信息（MI），fr \'Echet Inception Inteption距离（FID），峰值信号到峰值信号-noise比率（PSNR）和局部二进制模式矢量（LBPV）。结果表明，FID和LBPV与临床图像质量评分具有更强的关系。复制此工作的资源可在\ url {https://github.com/budai4medtech/miua2022}中获得。