The field of ultrasound image analysis and interpretation is rapidly advancing, driven by the development of innovative deep learning models and techniques. Recent research has focused on improving the accuracy and efficiency of ultrasound image analysis, with a particular emphasis on addressing the challenges posed by the modality's inherent limitations, such as speckle noise and limited standardized annotations. Notably, researchers are exploring the use of self-supervised and semi-supervised learning approaches to leverage large datasets and improve model generalizability. Additionally, there is a growing interest in developing models that can integrate information from multiple imaging modalities, such as ultrasound and X-ray, to enhance anatomical structure recovery and visualization. The development of more accurate and reliable ultrasound image analysis models has the potential to significantly impact clinical practice, enabling faster and more accurate diagnoses, and improving patient outcomes. Some noteworthy papers in this area include: OpenUS, which proposes a fully open-source foundation model for ultrasound image analysis, and SEMC, which introduces a novel structure-enhanced mixture-of-experts contrastive learning framework for ultrasound standard plane recognition. EchoAgent is also a notable work, presenting a guideline-centric reasoning agent for echocardiography measurement and interpretation. US-X Complete is another significant contribution, demonstrating a multi-modal approach to anatomical 3D shape recovery. Furthermore, ProPL pioneers the task of universal semi-supervised ultrasound image segmentation, and MUSSE-Net proposes a residual-aware, multi-stage unsupervised sequential deep learning framework for consistent strain estimation. Lastly, Automated Interpretable 2D Video Extraction from 3D Echocardiography presents an automated method to select standard 2D views from 3D cardiac ultrasound volumes.