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          <h1>AI-Driven Assessment of Echocardiographic Image Quality</h1>
          <h2><i>University of West London – School of Computing and Engineering</i></h2>
          <h3>In Collaboration with the <a href="https://www.imperial.ac.uk/nhli/" target="_blank" rel="noopener noreferrer">National Heart and Lung Institute</a></h3>
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            <h1><b>Supervisory Team</b></h1>
            <h6><a href="https://www.uwl.ac.uk/staff/massoud-zolgharni" target="_blank" rel="noopener noreferrer">Professor Massoud Zolgharni</a></h6>
            <h6><a href="https://www.imperial.ac.uk/people/d.francis" target="_blank" rel="noopener noreferrer">Professor Darrel Francis</a></h6>
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                <h1>Background:</h1>
                <h3>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.</h3>
                <h3>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.</h3>

                <h1>Research Questions:</h1>
                <h3>- Can machine learning algorithms be trained to assess image quality in echocardiography automatically?</h3>
                <h3>- What visual features or quality metrics correlate most strongly with expert evaluations?</h3>
                <h3>- How can automated image quality scoring be integrated into clinical workflows for immediate feedback?</h3>

                <h1>Aim and Objectives:</h1>
                <h3>This PhD project aims to develop an AI model capable of automatically assessing echocardiographic image quality. The specific objectives are to:</h3>
                <h3>- Define clinical and visual metrics for image quality with expert input.</h3>
                <h3>- Develop a quality-annotated dataset based on expert reviews.</h3>
                <h3>- Train and evaluate deep learning models to classify and score image quality.</h3>
                <h3>- Integrate quality assessment with downstream diagnostic pipelines.</h3>

                <h1>Methodology:</h1>
                <h3>- Collaborate with clinical experts to define grading criteria and label datasets.</h3>
                <h3>- Apply convolutional neural networks (CNNs) and attention-based models to evaluate static and video-mode echo data.</h3>
                <h3>- Benchmark model performance against expert-level scoring.</h3>
                <h3>- Perform ablation studies to identify critical image features.</h3>

                <h1>Clinical Partners:</h1>
                <h3>Collaboration with the School of Medicine at Imperial College London will ensure clinical validation and relevance of the developed system, including:</h3>
                <h3>- Access to annotated datasets and expert sonographers.</h3>
                <h3>- Validation and translation into real-world settings.</h3>

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                <h3>For more information about the project, please contact <a href="https://www.uwl.ac.uk/staff/massoud-zolgharni" target="_blank" rel="noopener noreferrer">Professor Massoud Zolgharni</a></h3>
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