vital-ultrasound/ai-echocardiography-for-low-resource-countries
Domaine:
healthcareCréateur:
vit
Hôte:
AI-assisted echocardiography for low-resource countries
AI-assisted echocardiography for low-resource countries
This work is 100% Reproducible, lead by github.com
## Summary
This repository contains documentation and code for AI-assisted echocardiography for low-resource countries.
This work presents 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 low income countries (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.
This work is based on the total product life cycle (TPLC) approach on AI/ML workflow from Good Machine Learning Practices established by the U.S. Food and Drug Administration, Health Canada, and the UK's Medicines and Healthcare products Regulatory Agency (MHRA).
## Data collection, validation and management :open_file_folder:
1. Video data from GE Venue Go GE and Probe 3SC-RS was collected with Portable Video Recorder. See more.
2. Creation and verification of annotations with VGG Image Annotator (VIA) software. See more.
3. Jupyter notebook :notebook: for data curation, selection and validation;
_**Fig 1.** Workflow for data annotation and validation._
## Deep learning pipeline :open_file_folder:
1. Model selection (MobileNetV1, MobileNetV2, SqueezeNet, EfficientNet, SqueezeNet, AlexNet, etc). See more.
2. Model training and tuning (Fig. 2). See more.
3. Model validation (performance evaluation and clinical evaluation).
4. AI-based device modification, and (perhaps) AI-based production model .
_**Fig 2.** Deep learning pipeline of the AI-empowered echocardiography._
## AI-based research system :open_file_folder:
Figure 3 illustrates the real-time AI-empowered clinical system based on EPIQ 7 ultrasound, X5-1 xMATRIX array transducer and USB fram …
Similaires
AI system for fetal ultrasound in low-resource settingsAI Diffusion in Low Resource Language CountriesMaternal fetal ultrasound planes from low-resource imaging settings in five African countriesA Machine Learning Case Study for AI-empowered echocardiography of Intensive Care Unit Patients in low- and middle-income countriesGeneralisability of fetal ultrasound deep learning models to low-resource imaging settings in five African countriesRole-Adapted Clinical Report Generation for Ultrasound Measurements in Low-Resource Settings