AI-Driven Assessment of Echocardiographic Image Quality
Supervisory Team
Background
Echocardiographic image quality plays a critical role in the reliability and accuracy of clinical interpretation. However, image acquisition is highly operator-dependent, and suboptimal image quality can compromise diagnostic outcomes.
There is an unmet need for automated, real-time assessment tools that can evaluate and flag poor-quality echocardiographic images at the point of acquisition.
Research Questions
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Can machine learning algorithms be trained to assess image quality in echocardiography automatically?
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What visual features or quality metrics correlate most strongly with expert evaluations?
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How can automated image quality scoring be integrated into clinical workflows for immediate feedback?
Aim
- Develop an AI model capable of automatically assessing echocardiographic image quality.
Objectives
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Define Metrics
- Define clinical and visual metrics for image quality with expert input.
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Dataset Development
- Develop a quality-annotated dataset based on expert reviews.
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Model Development
- Train and evaluate deep learning models to classify and score image quality.
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Integration
- Integrate quality assessment with downstream diagnostic pipelines.
Methodology
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Collaborate with clinical experts to define grading criteria and label datasets.
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Apply convolutional neural networks (CNNs) and attention-based models to evaluate static and video-mode echo data.
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Benchmark model performance against expert-level scoring.
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Perform ablation studies to identify critical image features.
Clinical Partners
- Imperial College London – School of Medicine