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FLUEnT: Transformer for detecting lung consolidations in videos using fused lung ultrasound encodings
Umair Khan1, Russell Thompson2, Jason Li3
1Department of Information Engineering and Computer Science, University of Trento, Trento, Italy.
Insights
A new AI framework, FLUEnT, enhances pediatric pneumonia diagnosis using fused lung ultrasound (LUS) data. This method improves the accuracy of detecting lung consolidations in children, aiding critical care decisions.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Pediatric Pulmonology
Background:
- Pneumonia is a leading cause of childhood mortality globally.
- Lung ultrasound (LUS) aids pediatric pneumonia diagnosis, but its use is limited by training needs.
- Artificial Intelligence (AI) offers a solution for automating LUS interpretation.
Purpose of the Study:
- To introduce FLUEnT, a novel AI framework for pediatric LUS video analysis.
- To detect lung consolidations in children using fused LUS encodings.
- To improve the accuracy and efficiency of pneumonia diagnosis in pediatric patients.
Main Methods:
- Developed FLUEnT, a transformer-based framework utilizing fused LUS encodings.
- Combined frame-level embeddings (variational autoencoder), ResNet-18 features, and metadata.
- Applied the framework for binary classification of lung consolidations in pediatric LUS videos.
Main Results:
- Achieved a mean balanced accuracy of 89.3% for detecting lung consolidations.
- Demonstrated an average improvement of 4.7% compared to individual encoding methods.
- Outperformed state-of-the-art models by an average of 8%.
Conclusions:
- The FLUEnT framework sets a new benchmark for LUS video analysis in pediatric pneumonia.
- AI-powered LUS interpretation can significantly improve diagnostic accuracy.
- This approach has the potential to reduce mortality in resource-limited settings.
Abstract:
Pneumonia is the leading cause of death among children around the world. According to WHO, a total of 740,180 lives under the age of five were lost due to pneumonia in 2019. Lung ultrasound (LUS) has been shown to be particularly useful for supporting the diagnosis of pneumonia in children and reducing mortality in resource-limited settings. The wide application of point-of-care ultrasound at the bedside is limited mainly due to a lack of training for data acquisition and interpretation. Artificial Intelligence can serve as a potential tool to automate and improve the LUS data interpretation process, which mainly involves analysis of hyper-echoic horizontal and vertical artifacts, and hypo-echoic small to large consolidations. This paper presents, Fused Lung Ultrasound Encoding-based Transformer (FLUEnT), a novel pediatric LUS video scoring framework for detecting lung consolidations using fused LUS encodings. Frame-level embeddings from a variational autoencoder, features from a spatially attentive ResNet-18, and encoded patient information as metadata combiningly form the fused encodings. These encodings are then passed on to the transformer for binary classification of the presence or absence of consolidations in the video. The video-level analysis using fused encodings resulted in a mean balanced accuracy of 89.3 %, giving an average improvement of 4.7 % points in comparison to when using these encodings individually. In conclusion, outperforming the state-of-the-art models by an average margin of 8 % points, our proposed FLUEnT framework serves as a benchmark for detecting lung consolidations in LUS videos from pediatric pneumonia patients.
