A Machine Learning Case Study for AI-empowered echocardiography of Intensive Care Unit Patients in low- and middle-income countries
This work addresses the problem of deploying AI-assisted echocardiography in resource-limited ICU settings, but it is incremental as it builds on existing methods with a focus on thinner models and specific data constraints.
The study tackled the challenge of clinical translation for a real-time AI-empowered echocardiography system in low- and middle-income countries by using thinner neural networks to classify apical four-chamber views from 2D ultrasound videos of 31 ICU patients, showing promising results for implementation with limited datasets.
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.