Deep Learning Applications in Surgery: Current Uses and Future Directions.

Journal: The American surgeon
Published Date:

Abstract

Deep learning (DL) is a subset of machine learning that is rapidly gaining traction in surgical fields. Its tremendous capacity for powerful data-driven problem-solving has generated computational breakthroughs in many realms, with the fields of medicine and surgery becoming increasingly prominent avenues. Through its multi-layer architecture of interconnected neural networks, DL enables feature extraction and pattern recognition of highly complex and large-volume data. Across various surgical specialties, DL is being applied to optimize both preoperative planning and intraoperative performance in new and innovative ways. Surgeons are now able to integrate deep learning tools into their practice to improve patient safety and outcomes. Through this review, we explore the applications of deep learning in surgery and related subspecialties with an aim to shed light on the practical utilization of this technology in the present and near future.

Authors

  • Miranda X Morris
    12277Duke University School of Medicine, Durham, NC, USA.
  • Aashish Rajesh
    Department of Surgery, University of Texas Health Science Center, San Antonio, TX, USA.
  • Malke Asaad
    From the Division of Plastic Surgery, Department of Surgery, Mayo Clinic; the Division of Plastic Surgery, Department of Surgery, Sidra Medicine; and the Department of Surgery, Weill-Cornell Medical College-Qatar.
  • Abbas Hassan
    Department of Plastic Surgery, 571198The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  • Rakan Saadoun
    Department of Plastic Surgery, 6595University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
  • Charles E Butler
    Department of Plastic & Reconstructive Surgery, the University of Texas MD Anderson Cancer Center, Houston, TX, USA.