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Machine Learning assisted systematic reviewing in orthopaedics.

Bart G Pijls1

  • 1Department of Orthopaedics, Leiden University Medical Center, Leiden, the Netherlands.

Journal of Orthopaedics
|December 13, 2023
PubMed
Summary

Machine learning significantly reduces workload in orthopaedic systematic reviews by efficiently retrieving relevant papers. This approach saves 60-70% of manual screening effort, identifying most papers within the first 10%.

Keywords:
ASReviewArtificial intelligenceMachine learningScreeningSystematic review

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Area of Science:

  • Medical Informatics
  • Evidence-Based Medicine
  • Orthopaedic Research

Background:

  • Systematic reviews are crucial in orthopaedics but are labor-intensive.
  • Machine learning (ML) offers potential to streamline the systematic review process.
  • This study evaluates ML-assisted systematic reviewing performance in orthopaedics.

Purpose of the Study:

  • To assess the effectiveness of ML-assisted systematic reviewing for retrieving relevant papers.
  • To quantify the work saved by ML compared to traditional methods in orthopaedic reviews.
  • To determine the performance of ML in different review complexity scenarios (easy, intermediate, advanced).

Main Methods:

  • Active learning for Systematic Reviews (ASReview) software was utilized.
  • ASReview was tested on three prior orthopaedic systematic reviews.
  • Performance was evaluated over 20 iterations, measuring percentage work saved (WSS) and relevant reference identification rate (RRF).

Main Results:

  • Percentage work saved at 95% recall (WSS@95) ranged from 50% (advanced) to 72% (easy/intermediate).
  • Percentage work saved at 100% recall (WSS@100) ranged from 37% (advanced) to 72% (easy).
  • Relevant references were identified within the first 10% of screened records (RRF@10) at rates of 58% (advanced) to 79% (easy).

Conclusions:

  • ML-assisted systematic reviewing is efficient for orthopaedic research, significantly reducing the burden.
  • The majority of relevant papers were identified early in the screening process (within 10%).
  • Substantial work savings (60-70%) are achievable, with all relevant papers found after screening 30-40%.