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Predictive modeling in spine surgery.

Azeem Tariq Malik1, Safdar N Khan1

  • 1Department of Orthopaedics, The Ohio State University Wexner Medical Center, Columbus, OH, USA.

Annals of Translational Medicine
|October 19, 2019
PubMed
Summary
This summary is machine-generated.

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Machine learning (ML) shows promise for improving spine surgery by aiding clinical decisions and predicting outcomes. This review explores the current literature on ML

Area of Science:

  • * Medical Technology
  • * Data Science in Healthcare

Background:

  • * Rising healthcare costs necessitate innovative solutions for value-based care delivery.
  • * Artificial intelligence (AI) and machine learning (ML) are increasingly discussed for assisting clinical decision-making and outcome prediction.
  • * ML applications in spine surgery are still in early stages of development and adoption.

Purpose of the Study:

  • * To review and synthesize existing literature on the validity and applicability of machine learning models in spine surgery.
  • * To assess the current state of ML in spine surgery practice.

Main Methods:

  • * Comprehensive literature search of studies discussing machine learning in spine surgery.
  • * Analysis of identified literature focusing on model validity and practical application.
Keywords:
Predictive modelingspinal fusionsspine surgery

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Main Results:

  • * Machine learning models demonstrate potential in various aspects of spine surgery, including diagnosis, prognosis, and treatment planning.
  • * Evidence for the widespread clinical validity and applicability of these models is still emerging.
  • * Further research is needed to establish robust ML tools for routine spine surgery.

Conclusions:

  • * Machine learning holds significant potential to enhance spine surgery outcomes and efficiency.
  • * The field requires further validation and integration to realize its full benefits in clinical practice.
  • * Continued research is crucial for advancing the use of ML in spine surgery.