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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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Fetal Face: Enhancing 3D Ultrasound Imaging by Postprocessing With AI Applications: Myth, Reality, or Legal Concerns?

G Tonni¹, G Grisolia², Silvia Tonni³

¹Department of Obstetrics and Neonatology, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), ASL of Reggio Emilia, Reggio Emilia, Italy.

Journal of Clinical Ultrasound : JCU

|October 25, 2024

Summary

This summary is machine-generated.

Artificial intelligence (AI) is transforming healthcare, especially in prenatal ultrasound diagnosis. Ensuring proper regulation and development of AI tools is crucial for accurate medical imaging and data management.

Keywords:

3D/4D ultrasound 5D ultrasound artificial intelligence (AI)facial clefting fetal face

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Area of Science:

Medical Imaging
Artificial Intelligence
Healthcare Technology

Background:

Artificial intelligence (AI) is increasingly utilized in medicine for data management.
Healthcare applications of AI are expanding, necessitating guidelines for responsible use.
International organizations and the European Union have issued recommendations for AI in healthcare.

Purpose of the Study:

To highlight the revolutionary impact of AI in prenatal ultrasound diagnosis.
To emphasize the need for regulation in AI-driven diagnostic enhancements.
To advocate for the development of AI applications ensuring accurate medical imaging.

Main Methods:

Review of current AI applications in medical data management and healthcare.
Analysis of AI's role in prenatal ultrasound diagnostics.
Examination of existing and proposed regulatory principles for AI in healthcare.

Main Results:

AI significantly enhances performance in managing metadata and big data in medicine.
AI is revolutionizing prenatal ultrasound diagnosis, improving diagnostic capabilities.
The potential for errors and misconduct necessitates regulatory frameworks for AI in healthcare.

Conclusions:

AI presents a transformative potential in medical diagnostics, particularly in prenatal ultrasound.
Robust regulation and development standards are essential for AI applications in healthcare to ensure accuracy and prevent misconduct.
Standardized AI development is key for reliable imaging acquisition and postprocessing in medical applications.