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Machine Learning in Nuclear Medicine: Part 1-Introduction.

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This article introduces machine learning (ML) in nuclear medicine, covering its history and common algorithms. It explains how ML can be applied to enhance nuclear medicine practices.

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Area of Science:

  • Nuclear Medicine
  • Artificial Intelligence
  • Data Science

Background:

  • Machine learning (ML) is increasingly relevant in medical imaging and diagnostics.
  • Understanding ML fundamentals is crucial for nuclear medicine professionals.
  • This article serves as an introductory guide to ML concepts within the field.

Purpose of the Study:

  • To provide a foundational understanding of machine learning (ML) for nuclear medicine.
  • To explore the historical development of ML algorithms.
  • To illustrate the practical applications of common ML algorithms in nuclear medicine.

Main Methods:

  • Review of machine learning history and foundational concepts.
  • Description of common machine learning algorithms (e.g., supervised, unsupervised, deep learning).
  • Illustrative examples of ML algorithm utility in nuclear medicine scenarios.

Main Results:

  • Historical overview of ML development.
  • Explanation of key ML algorithms and their mechanisms.
  • Demonstration of ML's potential benefits in nuclear medicine image analysis and interpretation.

Conclusions:

  • Machine learning offers significant potential to advance nuclear medicine.
  • Familiarity with ML algorithms is essential for future nuclear medicine research and practice.
  • This introductory part lays the groundwork for understanding ML's role in the field.