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IntroductionUltrasonography, or renal ultrasound, is a noninvasive medical imaging technique that uses high-frequency sound waves to visualize the kidneys, ureters, bladder, and surrounding tissues.Indications for Urinary System UltrasonographyUrinary system ultrasonography is indicated in various clinical scenarios, such as:Kidney Stones (Urolithiasis): To detect and monitor the size and presence of kidney or urinary tract stones.Hydronephrosis: To assess the dilation of the renal pelvis and...
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Deep Learning for Clinical Ultrasound Imaging: From Supervised Approaches to Foundation Models.

Keshi He, Donglai Wei, Bryan Ranger

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    Summary
    This summary is machine-generated.

    Foundation models, pre-trained on vast datasets, offer a paradigm shift for AI in ultrasound imaging. They promise to overcome limitations like scarce data and enhance diagnostic performance across diverse clinical applications.

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    Area of Science:

    • Artificial Intelligence
    • Medical Imaging
    • Ultrasound Technology

    Background:

    • Traditional deep learning models lack generalizability across domains.
    • Foundation models, pre-trained on extensive data, enable broad task support.
    • Ultrasound imaging is a low-cost, versatile clinical tool with potential for AI enhancement.

    Purpose of the Study:

    • To review deep learning (DL) applications in clinical ultrasound.
    • To highlight the potential of foundation models in ultrasound diagnostics.
    • To discuss challenges and future directions for DL in ultrasound.

    Main Methods:

    • Review of recent literature on ultrasound image analysis using DL.
    • Emphasis on supervised, self-supervised, discriminative, generative, and foundation models.
    • Summary of DL-based ultrasound clinical applications.

    Main Results:

    • Foundation models show promise for enhancing diagnostic performance in ultrasound.
    • DL advancements include supervised, self-supervised, and generative AI approaches.
    • Key challenges include limited labeled data and domain variability.

    Conclusions:

    • Foundation models can significantly expand the scope and improve diagnostic accuracy of ultrasound.
    • Addressing challenges like data scarcity is crucial for wider adoption.
    • Future research should focus on advancing DL techniques for clinical ultrasound.