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Radiation: Applications01:17

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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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Quantifying Cognitive Decrements Caused by Cranial Radiotherapy
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Artificial intelligence uncertainty quantification in radiotherapy applications - A scoping review.

Kareem A Wahid1, Zaphanlene Y Kaffey2, David P Farris3

  • 1Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

Radiotherapy and Oncology : Journal of the European Society for Therapeutic Radiology and Oncology
|September 19, 2024
PubMed
Summary
This summary is machine-generated.

This review highlights the limited diversity in artificial intelligence uncertainty quantification for radiotherapy, primarily focusing on auto-contouring. Future research should explore additional methods like conformal prediction to build clinician trust.

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Area of Science:

  • Radiotherapy
  • Artificial Intelligence
  • Uncertainty Quantification

Background:

  • Clinician trust in artificial intelligence (AI) for radiotherapy (RT) is hindered by a lack of effective uncertainty quantification (UQ).
  • This study addresses the need for comprehensive UQ methods in RT to enhance AI model reliability.

Purpose of the Study:

  • To conduct a scoping review of existing literature on UQ in RT.
  • To identify gaps and areas for improvement in current UQ methods.
  • To determine future research directions for UQ in RT.

Main Methods:

  • Adherence to PRISMA-ScR guidelines for a scoping review.
  • Systematic search across seven databases, supplemented by manual curation up to January 2024.
  • Data extraction focused on study, RT, AI, and UQ characteristics.

Main Results:

  • 56 articles from 2015-2024 were analyzed, covering 10 RT application domains, with auto-contouring being most prevalent (50%).
  • Head and neck cancer was the most studied disease site (32%). Imaging data was used in 91% of studies, while RT dose information was incorporated in only 13%.
  • Failure detection was the primary UQ application (60%), with Monte Carlo dropout (32%) and ensembling (16%) as common methods. Code/datasets were shared in only 45% of studies.

Conclusions:

  • A significant lack of diversity exists in UQ for RT applications beyond auto-contouring.
  • There is a critical need to investigate novel UQ methods, such as conformal prediction.
  • Findings may guide the development of standardized reporting and implementation guidelines for UQ in RT.