Deep Learning Applications in Surgery: Current Uses and Future Directions.
Miranda X MorrisAashish RajeshMalke AsaadAbbas HassanRakan SaadounCharles E ButlerPublished in: The American surgeon (2022)
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.
Keyphrases
- deep learning
- machine learning
- patient safety
- minimally invasive
- artificial intelligence
- coronary artery bypass
- quality improvement
- convolutional neural network
- neural network
- big data
- surgical site infection
- healthcare
- current status
- patients undergoing
- acute coronary syndrome
- percutaneous coronary intervention
- data analysis
- drug induced