Sensor technology and machine learning to guide clinical decision making in plastic surgery.

Journal: Journal of plastic, reconstructive & aesthetic surgery : JPRAS
Published Date:

Abstract

Subjective clinical evaluations are deeply rooted in medical practice. Recent advances in sensor technology facilitate the acquisition of extensive amounts of objective physiological data that can serve as a surrogate for subjective assessments. Along with sensor technology, a branch of artificial intelligence, known as machine learning, has provided decisive advances in several areas of medicine due to its pattern recognition and outcome prediction abilities. The assimilation of machine learning algorithms into sensor technology can substantially improve our current diagnostic and treatment competencies. This review explores available data on the use of sensor technology and machine learning in areas of interest for plastic surgeons, updates current knowledge on the most recent technological advances, and provides a new perspective on the field.

Authors

  • Francisco R Avila
    Division of Plastic Surgery, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL, 32224, USA.
  • Sahar Borna
    Division of Plastic Surgery, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL, 32224, USA.
  • Christopher J McLeod
    Department of Cardiovascular Medicine, Mayo Clinic, Jacksonville, FL, USA.
  • Charles J Bruce
    Department of Cardiovascular Medicine, Mayo Clinic, Jacksonville, FL, USA.
  • Rickey E Carter
    Department of Health Sciences Research, Mayo Clinic, Jacksonville, Florida.
  • Cesar A Gomez-Cabello
    Division of Plastic Surgery, Mayo Clinic, Jacksonville, FL, USA.
  • Sophia M Pressman
    Division of Plastic Surgery, Mayo Clinic, Jacksonville, FL, USA.
  • Syed Ali Haider
    Division of Plastic Surgery, Mayo Clinic, Jacksonville, FL, USA.
  • Antonio Jorge Forte
    Division of Plastic Surgery, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL, 32224, USA. ajvforte@yahoo.com.br.