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Machine Learning and Surgical Outcomes Prediction: A Systematic Review.

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Machine learning (ML) models show promise in predicting surgical outcomes by analyzing complex medical data. Future research needs standardized reporting and quality standards for optimal clinical application.

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Area of Science:

  • Medical Informatics
  • Surgical Research
  • Artificial Intelligence in Medicine

Background:

  • Machine learning (ML) is increasingly utilized for analyzing complex medical data to enhance personalized patient care.
  • The application of ML in predicting surgical outcomes is a rapidly evolving field.
  • Critical evaluation of current ML research in surgery is essential for future advancements.

Purpose of the Study:

  • To critically examine the current state of machine learning in predicting surgical outcomes.
  • To evaluate the quality of existing research on ML in surgery.
  • To propose areas for improvement in future ML applications within surgical practice.

Main Methods:

  • A systematic review was performed adhering to the PRISMA checklist.
  • Literature search conducted on PubMed, MEDLINE, and Embase databases using keywords 'machine learning' and 'surgery' for papers published between 2015 and 2020.
  • Inclusion and exclusion criteria were applied to filter relevant studies from an initial pool of 2677 papers.

Main Results:

  • 45 studies met the criteria, representing 14 surgical subspecialties, with neurosurgery being most frequent.
  • Random forest, artificial neural network, and logistic regression were the most common ML algorithms employed.
  • ML models demonstrated improved accuracy in predicting outcomes such as mortality, complications, and quality of life compared to conventional methods, as measured by AUC.

Conclusions:

  • Machine learning models offer significant potential for improving individualized patient care by leveraging clinical data.
  • Current limitations include heterogeneity in outcome reporting and variable research quality.
  • Future research should prioritize standardized outcome reporting and establish minimum quality standards for ML studies in surgery.