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Deep Learning Application in Spinal Implant Identification.

Hee-Seok Yang1, Kwang-Ryeol Kim2, Sungjun Kim3

  • 1Department of Neurosurgery, Seoul Barunsesang Hospital, Seoul, South Korea.

Spine
|February 3, 2021
PubMed
Summary
This summary is machine-generated.

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Deep learning effectively identifies spinal implants in radiographs, showing high precision and recall. This artificial intelligence application can enhance clinical practice and patient care in spine surgery.

Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Healthcare
  • Spine Surgery Technology

Background:

  • Deep learning (DL) shows promise in medical imaging but has faced challenges in successful application.
  • Previous attempts to apply DL to medical images have yielded limited success.
  • This study aimed to demonstrate the effectiveness and clinical utility of DL in the medical field.

Purpose of the Study:

  • To showcase the clinical usefulness of deep learning algorithms.
  • To identify previous spinal implants using deep learning.
  • To evaluate the performance of different DL models in implant detection.

Main Methods:

  • Retrospective observational study using 2894 lumbar spine radiographs (AP and lateral).
  • Development of DL algorithms including a transfer learning model, Google AutoML, and Apple Create ML.

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  • Training and evaluation of models for identifying five types of spinal implants, measuring precision and recall.

    • Main Results:

    • All tested DL models demonstrated strong performance in identifying pedicle screw implants.
    • Conventional transfer learning achieved 98.7% precision and 98.2% recall on lateral radiographs.
    • Lateral radiography generally yielded higher precision and recall than AP radiography across all models.

    Conclusions:

    • Deep learning applications are effective for identifying spinal implants.
    • This technology can be utilized by clinicians to improve medical practice and patient outcomes.
    • The study highlights the potential of machine learning-based DL tools in enhancing surgical care.