Deep learning ultrasound imaging, Existing research predominantly focuses on supervised learning that relies on high-quality paired references. Nov 1, 2025 · ObjectiveThis systematic review critically appraises the current landscape of physics-aware artificial intelligence (AI) in medical imaging for quantitative biomarker mapping in Metabolic dysfunction-associated steatotic liver disease (MASLD) and its progressive form, MASH. Oct 11, 2025 · In this section, we detail the imaging process of deep learning-based 3D ultrasound imaging, with a focus on identifying these challenges and analyzing their root causes. . Apr 8, 2025 · We provide a comprehensive overview of deep learning-based ultrasound image segmentation methods, evaluation metrics, and common ultrasound datasets, hoping to explain the advantages and disadvantages of each method, summarize its achievements, and discuss challenges and future trends. It focuses on deep learning and radiomics applications across ultrasound, CT, and MRI. MethodsA PRISMA 2020-guided Ultrasound plane-wave (PW) imaging achieves an ultra-high frame rate at the expense of degraded image quality. Deep learning-based methods have emerged as a promising avenue for enhancing PW imaging quality. Discussion In this study, we developed an AI-assisted diagnostic system using machine learning to detect PTC based on ultrasound imaging. This is challenging in real-world scenarios, highlighting the need for An overview of the current state of deep learning research in breast cancer imaging is given, showing similar and even better performances of DL algorithms compared to radiologists, although it is clear that large trials are needed, especially for ultrasound and magnetic resonance imaging, to exactly determine the added value of DL in breast Cancer imaging. In this report, the authors review current DL approaches and research directions in rapidly advancing ultrasound technology and present their outlook on future directions and trends for DL techniques to further improve diagnosis, reduce health care cost, and optimize ultrasound clinical workflow. Our goal is to provide the reader with a broad understanding of the possible impact of deep learning methodologies on many aspects of ultrasound imaging. Furthermore, we implemented a smartphone-compatible application that enables diagnostic evaluation from images captured via mobile devices, maintaining high diagnostic accuracy. This paper proposes an end-to-end deep learning framework for the automated detection and classification of kidney stones from CT and ultrasound images. Aug 21, 2019 · In this article, we consider deep learning strategies in ultrasound systems, from the front end to advanced applications. Apr 1, 2019 · Deep learning has recently emerged as the leading machine learning tool in various research fields, and especially in general imaging analysis and computer vision. This review first outlines the workflow and design principles of deep learning based ultrasound clinical applications, with emphasis on advancements in supervised and self-supervised learning, discriminative models, generative AI models, foundation models, and other miscellaneous models. Jul 1, 2024 · In this survey, we are about to explore the application of deep learning in medical ultrasound imaging, spanning from image reconstruction to clinical diagnosis. Expand 127 PDF 1 Excerpt Takeaway Deep learning–augmented HRUS has the potential to make ultrasound not just safer and more affordable, but also smarter, faster, and more reliable across specialties like oncology Jan 6, 2026 · To break these barriers, this paper proposes an explainable multimodal deep learning model that combines ultrasound images with structured clinical features through a cross-attention fusion mechanism. Deep learning also shows huge potential for various automatic US image analysis tasks.


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