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Classification of Bones

Classification of Bones

The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
Long and Short Bones
The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The...

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Bone and Soft Tissue Tumors: Horizons in Radiomics and Artificial Intelligence.

Michael L Richardson¹, Behrang Amini², Paul E Kinahan³

¹Department of Radiology, University of Washington, 4245 Roosevelt Way NE, Seattle, WA 98105, USA.

Radiologic Clinics of North America

|March 3, 2022

Summary

Artificial intelligence (AI) enhances medical imaging quality and efficiency while reducing radiation exposure. AI-driven radiomics aims to personalize cancer diagnosis and treatment for improved patient outcomes.

Keywords:

Artificial intellligence Radiomics Soft tissue tumor

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Area of Science:

Medical imaging
Artificial intelligence
Radiomics
Precision medicine

Background:

Artificial intelligence (AI) offers potential to improve medical imaging techniques like CT, MR, and PET/CT.
AI can enhance image quality, reduce scan times, and lower radiation and contrast agent doses.
AI tools may assist radiologists in workflow efficiency and interpretation speed.

Purpose of the Study:

To explore the multifaceted applications of AI in medical imaging and radiomics.
To highlight AI's role in improving diagnostic accuracy and treatment personalization.
To discuss the potential of radiomics in achieving precision medicine for oncological conditions.

Main Methods:

Review of AI applications in CT, MR, and PET/CT image enhancement.
Analysis of AI's impact on radiologist workflow and interpretation.
Exploration of radiomics principles and their integration with AI for clinical decision-making.

Main Results:

AI demonstrates capability in improving image quality and reducing imaging parameters.
AI can streamline radiologist tasks, leading to decreased interpretation times.
Future AI applications may include automated tumor detection, classification, and segmentation.

Conclusions:

AI integration in medical imaging promises significant advancements in diagnostic and therapeutic strategies.
Radiomics, powered by AI, is pivotal for realizing precision medicine in oncology.
AI-assisted radiomics can guide biopsies, tumor characterization, prognosis prediction, and treatment response assessment.