Advanced Deep Learning Approaches for Automated Assessment of Ultrasound Imaging (Lungs and Heart)

Ultrasound is a widely used imaging modality to evaluate various anatomical structures in the human body, such as the lungs and heart, due to its non-invasive nature and real-time imaging capabilities. However, the effectiveness of these evaluations is highly dependent on the manual annotation/segmentation of visual patterns. In lung assessments, patterns such as hyper-echoic horizontal and vertical artifacts and hypo-echoic consolidations are crucial for diagnosing both adult and neonatal respiratory conditions. Similarly, in cardiac evaluations, ultrasound imaging facilitates the visualization of anatomical structures, including the endocardium, myocardium, and atrium of the left ventricle, which are essential for the measurement of clinical indices such as ejection fraction (EF). Despite the advancements in ultrasound technology, manual annotation remains the standard practice in clinical routines due to the lack of fully automated AI solutions. However, manual annotation/segmentation of ultrasound patterns is not only time-consuming but also prone to inter-observer variability (IOV). Given with these challenges, evaluating inter-rater reliability across multiple operators, medical centers, and diverse patient populations is crucial for advancing lung ultrasound (LUS) diagnostics and improving patient outcomes. Additionally, the integration of AI to reduce the impact of IOV in ultrasound pattern interpretations has not been fully explored in the literature. Moving forward, many stateof- the-art studies have primarily focused on the interpretation of LUS patterns in adults, resulting in a notable gap in research concerning neonates. This thesis addresses this gap by introducing an advanced methodology aimed at standardizing and automating the interpretation of LUS patterns in neonates. To achieve this, various deep learning approaches, including classical neural networks and advanced transformer-based models, are employed. Additionally, domain adaptation techniques are introduced to facilitate the transfer of knowledge from adult LUS assessments to neonates. Furthermore, IOV also contributes to inconsistencies in data distribution, leading to an unequal representation of different classes within the dataset. To address these challenges, this thesis explores the application of generative AI, emphasizing the techniques that could effectively balance the data distributions. Building on this foundation, this thesis examines the use of generative AI models for the automated segmentation of left ventricle (LV ) regions to mitigate the effects of IOV. The proposed segmentation method was rigorously evaluated through qualitative and quantitative analyses, setting a new benchmark for future studies by demonstrating improved performance of EF estimation over state-of-the-art techniques. Lastly, this thesis introduces a novel approach that leverages generative AI models for automated labeling of LV regions using adjacent anatomical structures, such as utilizing the myocardium to segment the endocardium region or vice versa. This novel approach significantly reduces the need for manual labeling, ultimately minimizing IOV and saving time in clinical practice.