:page_facing_up: A Machine Learning Case Study for AI-empowered echocardiography of Intensive Care Unit Patients in low- and middle-income countries

Preprint2023 [![article](https://img.shields.io/badge/article-arXiv-orange.svg)](https://arxiv.org/abs/2212.14510) [![GitHub Actions Status](https://github.com/vital-ultrasound/2022-echocardiography-proceedings/workflows/CI-LaTeX/badge.svg)](https://github.com/vital-ultrasound/2022-echocardiography-proceedings/actions) [![new-literature](https://img.shields.io/badge/abstract-CIPDF-blue.svg)](https://github.com/vital-ultrasound/preprint2023/blob/pdfs/preprint2023.pdf) (This work is 100% Reproducible)

Abstract

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.

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BibTeX to cite

@misc{https://doi.org/10.48550/arxiv.2212.14510,
  author = {Xochicale, Miguel and 
        Thwaites, Louise and 
            Yacoub, Sophie and 
            Pisani, Luigi and 
            Phung, Tran Huy Nhat and 
            Kerdegari, Hamideh and 
            King, Andrew and 
            Gomez, Alberto}, 
  title = {A Machine Learning Case Study for AI-empowered echocardiography of 
           Intensive Care Unit Patients in low- and middle-income countries},
  doi = {10.48550/ARXIV.2212.14510},
  url = {https://arxiv.org/abs/2212.14510},
  keywords = {Medical Physics (physics.med-ph), 
          Machine Learning (cs.LG), 
          Image and Video Processing (eess.IV), 
          FOS: Physical sciences, 
              FOS: Physical sciences, 
          FOS: Computer and information sciences, 
          FOS: Electrical engineering, electronic engineering, information engineering},
  publisher = {arXiv},
  year = {2022},
  copyright = {Creative Commons Attribution 4.0 International}
}

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