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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Artificial intelligence (AI) integration in medicine is rapidly advancing.
  • Neuro-oncology increasingly utilizes AI for complex diagnostic tasks.
  • Brain metastases (BM) imaging evaluation presents significant challenges.

Purpose of the Study:

  • To review the application of AI, specifically machine learning (ML) and deep learning (DL), in the imaging evaluation of brain metastases (BM).
  • To analyze AI's role in segmentation, differential diagnosis, and prognostic prediction for BM.
  • To assess the current state and future potential of AI in neuro-oncological imaging.

Main Methods:

  • Systematic literature search of PubMed for studies published within the last 5 years.
  • Categorization and analysis of retrieved literature based on clinical tasks: segmentation, differential diagnosis, and prognostic prediction.
  • Synthesis of evidence on AI capabilities in detecting, segmenting, and diagnosing BM.

Main Results:

  • AI demonstrates capabilities in automatic detection and segmentation of BM using advanced imaging.
  • AI aids in differentiating BM from other intracranial lesions.
  • AI shows emerging potential in predicting prognosis and new metastatic development.

Conclusions:

  • AI enhances diagnostic efficiency and reproducibility in neuro-oncological imaging.
  • AI provides valuable insights for personalized treatment planning.
  • AI in neuro-oncological imaging is nascent with significant potential for future development.