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Radiation: Applications

Radiation: Applications

The average temperature of Earth is the subject of much current discussion. Earth is in radiative contact with both the Sun and dark space; it receives almost all its energy from the radiation of the Sun and reflects some of it into outer space. Dark space is very cold, about 3 K, so Earth radiates energy into it. For instance, heat transfer occurs from soil and grasses, the rate of which can be so rapid that frost can occur on clear summer evenings, even in warm latitudes.
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Related Experiment Video

Updated: Oct 26, 2025

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

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

Published on: August 16, 2020

Machine learning applications in radiation oncology.

Matthew Field^1,2, Nicholas Hardcastle^3,4, Michael Jameson^5,6

¹South Western Sydney Clinical School, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia.

Physics and Imaging in Radiation Oncology

|July 26, 2021

Summary

This summary is machine-generated.

Machine learning (ML) enhances radiation oncology by automating tasks and improving cancer treatment outcomes. Standardization and collaboration are key to integrating ML into clinical workflows for better patient care.

Keywords:

Artificial intelligence Automation Data mining Machine learning Radiation therapy

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Area of Science:

Oncology
Medical Physics
Artificial Intelligence

Background:

Machine learning (ML) is increasingly impacting radiation oncology research and industry.
Diverse data, including 3D imaging and radiation dose delivery, offer potential for automation and treatment improvement.
Advancements in radiation oncology necessitate ML integration, requiring investment in data quality, extraction, software, and clinical expertise.

Purpose of the Study:

To provide an overview of ML concepts.
To review advances in applying ML to radiation oncology.
To discuss the integration of ML techniques into radiation oncology workflows.

Main Methods:

Literature review of ML applications in radiation oncology.
Analysis of key areas within the radiation oncology workflow where ML can be applied.
Discussion on the importance of data standardization and interdisciplinary collaboration.

Main Results:

ML has early applications in radiation oncology due to the repetitive nature of tasks currently requiring human review.
Standardized data management of imaging and radiation dose data is crucial for ML research and clinical integration.
ML can significantly impact efficiency, treatment consistency, and patient outcomes in radiation oncology.

Conclusions:

ML offers significant potential to enhance radiation oncology workflows, improving efficiency and patient outcomes.
Standardized data and collaboration between experts are essential for successful ML implementation.
Physicists play a vital role in facilitating the technical integration of ML into clinical practice.