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Ultrasonography

Ultrasonography

Ultrasonography is an imaging technique that uses high-frequency sound waves to visualize the body's internal structures. It is a non-invasive and safe procedure that does not involve the use of ionizing radiation, making it widely used in various medical fields. Ultrasonography is used to study heart function, blood flow in the neck or extremities, certain conditions such as gallbladder disease, and fetal growth and development.
During an ultrasonography procedure, a handheld device called...

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Upstream Machine Learning in Radiology.

Christopher M Sandino¹, Elizabeth K Cole¹, Cagan Alkan¹

¹Department of Electrical Engineering, Stanford University, 350 Serra Mall, Stanford, CA 94305, USA.

Radiologic Clinics of North America

|October 25, 2021

Summary

This summary is machine-generated.

Artificial intelligence (AI) and machine learning (ML) can enhance radiology beyond image interpretation. This review explores AI/ML applications in upstream imaging processes like modality selection, data acquisition, and image reconstruction for improved practice.

Keywords:

Artificial intelligence Deep learning Image reconstruction Medical imaging

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Area of Science:

Radiology
Medical Imaging
Artificial Intelligence
Machine Learning

Background:

Artificial intelligence (AI) and machine learning (ML) are increasingly impacting medical practices.
Current focus in radiology is primarily on AI/ML for image interpretation.
Upstream components of the imaging pipeline remain underexplored for AI/ML applications.

Purpose of the Study:

To review the applications of AI/ML in upstream components of the radiology imaging pipeline.
To highlight the potential impact of AI/ML across various stages before image interpretation.
To demonstrate the versatility of AI/ML in different imaging modalities.

Main Methods:

Literature review of AI/ML applications in radiology.
Categorization of AI/ML applications based on their position in the imaging pipeline (upstream vs. interpretation).
Analysis of AI/ML impact across ultrasound, computed tomography (CT), and magnetic resonance imaging (MRI).

Main Results:

AI/ML can optimize exam modality selection and hardware design.
AI/ML facilitates improvements in exam protocol selection, data acquisition, and image reconstruction.
Significant potential for AI/ML to enhance image processing techniques.

Conclusions:

AI/ML offers transformative potential across the entire radiology imaging pipeline, not just interpretation.
Upstream applications of AI/ML can lead to more efficient and effective imaging practices.
The integration of AI/ML across modalities like ultrasound, CT, and MRI promises substantial advancements in radiology.