Abstract
Background:
Sub-Saharan Africa (SSA) bears the highest global burden of under-5 mortality, with congenital heart disease (CHD) as a major contributor. Despite advancements in high-income countries, CHD-related mortality in SSA remains largely unchanged due to limited diagnostic capacity and centralized health care. While pulse oximetry aids early detection, confirmation typically relies on echocardiography, a procedure constrained by a shortage of specialized personnel. Artificial intelligence (AI) offers a promising solution to bridge this diagnostic gap.
Objective:
This study aims to develop an AI-assisted echocardiography system that enables nonexpert operators, such as nurses, midwives, and medical doctors, to perform basic cardiac ultrasound sweeps on neonates suspected of CHD and extract accurate cardiac images for remote interpretation by a pediatric cardiologist.
Methods:
The study will use a 2-phase approach to develop a deep learning model for real-time cardiac view detection in neonatal echocardiography, utilizing data from St. Padre Pio Hospital in Cameroon and the Red Cross War Memorial Children’s Hospital in South Africa to ensure demographic diversity. In phase 1, the model will be pretrained on retrospective data from nearly 500 neonates (0-28 days old). Phase 2 will fine-tune the model using prospective data from 1000 neonates, which include background elements absent in the retrospective dataset, enabling adaptation to local clinical environments. The datasets will consist of short and continuous echocardiographic video clips covering 10 standard cardiac views, as defined by the American Society of Echocardiography. The model architecture will leverage convolutional neural networks and convolutional long short-term memory layers, inspired by the interleaved visual memory framework, which integrates fast and slow feature extractors via a shared temporal memory mechanism. Video preprocessing, annotation with predefined cardiac view codes using Labelbox, and training with TensorFlow and PyTorch will be performed. Reinforcement learning will guide the dynamic use of feature extractors during training. Iterative refinement, informed by clinical input, will ensure that the model effectively distinguishes correct from incorrect views in real time, enhancing its usability in resource-limited settings.
Results:
Retrospective data collection for the project began in September 2024, and to date, data from 308 babies have been collected and labeled. In parallel, the initial model framework has been developed and training initiated using a subset of the labeled data. The project is currently in the intensive execution phase, with all objectives progressing in parallel and final results expected within 10 months.
Conclusions:
The AI-assisted echocardiography model developed in this project holds promise for improving early CHD diagnosis and care in SSA and other low-resource settings.
International Registered Report Identifier (IRRID):DERR1-10.2196/75270
JMIR Res Protoc 2025;14:e75270
Sub-Saharan Africa (SSA) bears the highest global burden of under-5 mortality, with congenital heart disease (CHD) as a major contributor. Despite advancements in high-income countries, CHD-related mortality in SSA remains largely unchanged due to limited diagnostic capacity and centralized health care. While pulse oximetry aids early detection, confirmation typically relies on echocardiography, a procedure constrained by a shortage of specialized personnel. Artificial intelligence (AI) offers a promising solution to bridge this diagnostic gap.
Objective:
This study aims to develop an AI-assisted echocardiography system that enables nonexpert operators, such as nurses, midwives, and medical doctors, to perform basic cardiac ultrasound sweeps on neonates suspected of CHD and extract accurate cardiac images for remote interpretation by a pediatric cardiologist.
Methods:
The study will use a 2-phase approach to develop a deep learning model for real-time cardiac view detection in neonatal echocardiography, utilizing data from St. Padre Pio Hospital in Cameroon and the Red Cross War Memorial Children’s Hospital in South Africa to ensure demographic diversity. In phase 1, the model will be pretrained on retrospective data from nearly 500 neonates (0-28 days old). Phase 2 will fine-tune the model using prospective data from 1000 neonates, which include background elements absent in the retrospective dataset, enabling adaptation to local clinical environments. The datasets will consist of short and continuous echocardiographic video clips covering 10 standard cardiac views, as defined by the American Society of Echocardiography. The model architecture will leverage convolutional neural networks and convolutional long short-term memory layers, inspired by the interleaved visual memory framework, which integrates fast and slow feature extractors via a shared temporal memory mechanism. Video preprocessing, annotation with predefined cardiac view codes using Labelbox, and training with TensorFlow and PyTorch will be performed. Reinforcement learning will guide the dynamic use of feature extractors during training. Iterative refinement, informed by clinical input, will ensure that the model effectively distinguishes correct from incorrect views in real time, enhancing its usability in resource-limited settings.
Results:
Retrospective data collection for the project began in September 2024, and to date, data from 308 babies have been collected and labeled. In parallel, the initial model framework has been developed and training initiated using a subset of the labeled data. The project is currently in the intensive execution phase, with all objectives progressing in parallel and final results expected within 10 months.
Conclusions:
The AI-assisted echocardiography model developed in this project holds promise for improving early CHD diagnosis and care in SSA and other low-resource settings.
International Registered Report Identifier (IRRID):DERR1-10.2196/75270
JMIR Res Protoc 2025;14:e75270
| Original language | English |
|---|---|
| Article number | e75270 |
| Pages (from-to) | 1-20 |
| Number of pages | 20 |
| Journal | JMIR Research Protocol |
| Volume | 14 |
| Early online date | 30 Oct 2025 |
| DOIs | |
| Publication status | Published (in print/issue) - 30 Oct 2025 |
Bibliographical note
©Aminkeng Zawuo Leke, Lionel Landry Sop Deffo, Yunkavi Sabastian Wirsiy, Thomas Aldersley, Thomas Day, Andrew P King, Patrick McAllister, Michel N Maboh, John Lawrenson, Cabral Tantchou, Bernhard Kainz, Frank Casey, Raymond Bond, Dewar Finlay, Ngoe Kelson Tchinda, Armstrong Obale, Frunwi Ndeh Mugri, Liesl Zühlke, Helen Dolk. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 30.10.2025.Funding
We acknowledge the National Institutes of Health (grant award number 1U01HL172179-01). The content of this article is solely our responsibility and does not necessarily represent the official views of the US National Institutes of Health.
Keywords
- AI-assisted echocardiography
- Congenital heart defect (CHD) screening
- Neonatal cardiac imaging;
- Sub-Saharan Africa healthcare
- Telemedicine and AI integration
- Artificial Intelligence
- Humans
- Echocardiography - methods
- Sub-Saharan Africa health care
- Africa South of the Sahara
- telemedicine and AI integration
- Image Processing, Computer-Assisted - methods
- Deep Learning
- Heart Defects, Congenital - diagnostic imaging - diagnosis
- congenital heart disease (CHD) screening
- Retrospective Studies
- Infant, Newborn
- neonatal cardiac imaging
- Heart Defects, Congenital/diagnostic imaging
- Echocardiography/methods
- Image Processing, Computer-Assisted/methods
