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Oncologic Applications of Artificial Intelligence and Deep Learning Methods in CT Spine Imaging-A Systematic Review.

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Artificial intelligence (AI) in computed tomography (CT) imaging shows promise for spinal oncology, aiding in cancer detection, classification, and outcome prediction. Further research is needed to confirm its clinical effectiveness and integration into practice.

Keywords:
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Area of Science:

  • Medical Imaging
  • Oncology
  • Artificial Intelligence

Background:

  • Deep learning and AI show potential in spinal oncology using CT imaging.
  • Enhancing diagnostic accuracy, treatment planning, and patient outcomes are key areas of focus.

Purpose of the Study:

  • To systematically review artificial intelligence (AI) applications in computed tomography (CT) imaging for spinal tumors.
  • To synthesize evidence on AI's role in detection, classification, prognostication, and treatment planning.

Main Methods:

  • A PRISMA-guided systematic search identified 33 relevant studies.
  • Studies were categorized based on AI application: detection, classification, prognostication, and treatment planning.

Main Results:

  • AI applications included detecting spinal malignancies (36.4%), classification (33.3%), and prognostication (18.2%).
  • Classification studies utilized machine learning for lesion differentiation and radiomics for biomarker analysis.
  • Prognostic studies focused on predicting complications and treatment outcomes.

Conclusions:

  • AI in CT imaging offers potential benefits for workflow efficiency and decision-making in spinal oncology.
  • Limitations include generalizability, interpretability, and clinical integration challenges.
  • Further research is essential to validate AI's clinical effectiveness and optimize its routine use.