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'Machine Learning' May Contribute to New Advances in Plastic Surgery

With an ever-increasing volume of electronic data being collected by the healthcare system, researchers are exploring the use of machine learning—a subfield of artificial intelligence—to improve medical care and patient outcomes. An overview of machine learning and some of the ways it could contribute to advancements in plastic surgery are presented in a special topic article in the May issue of Plastic and Reconstructive Surgery®, the official medical journal of the American Society of Plastic Surgeons (ASPS).

"Machine learning has the potential to become a powerful tool in plastic surgery, allowing surgeons to harness complex clinical data to help guide key clinical decision-making," write Dr. Jonathan Kanevsky of McGill University, Montreal, and colleagues. They highlight some key areas in which machine learning and "Big Data" could contribute to progress in plastic and reconstructive surgery.

Machine Learning Shows Promise in Plastic Surgery Research and Practice
Machine learning analyzes historical data to develop algorithms capable of knowledge acquisition. Dr. Kanevsky and coauthors write, "Machine learning has already been applied, with great success, to process large amounts of complex data in medicine and surgery." Projects with healthcare applications include the IBM Watson Health cognitive computing system and the American College of Surgeons' National Surgical Quality Improvement Program.

Dr. Kanevsky and colleagues believe that plastic surgery can benefit from similar "objective and data-driven machine learning approaches"—particularly with the availability of the ASPS's 'Tracking Operations and Outcomes for Plastic Surgeons' (TOPS) database. The authors highlight five areas where machine learning shows promise for improving efficiency and clinical outcomes:

  • Burn Surgery. A machine learning approach has already been developed to predict the healing time of burns, providing an effective tool for assessing burn depth. Algorithms could also be developed to enable rapid prediction of percentage of body surface area burned—a critical piece of information for patient resuscitation and surgical planning.
  • Microsurgery. A postoperative microsurgery application has been developed to monitor blood perfusion of tissue flaps, based on smartphone photographs. In the future, algorithms may be developed to aid in suggesting the best reconstructive surgery approach for individual patients.
  • Craniofacial Surgery. Machine learning approaches for automated diagnosis of infant skull growth defects (craniosynostosis) have been developed. Future algorithms may be useful for identifying known and unknown genes responsible for cleft lip and palate.
  • Hand and Peripheral Nerve Surgery. Machine learning approaches may be useful in predicting the success of tissue-engineered nerve grafts, developing automated controllers for hand and arm neuroprostheses in patients with high spinal cord injuries, and improving planning and outcome prediction in hand surgery.
  • Aesthetic Surgery. Machine learning also has potential applications in cosmetic surgery—for example, predicting and simulating the outcomes of aesthetic facial surgery and reconstructive breast surgery.

The authors also foresee useful applications of machine learning to improve plastic surgery training. However, they emphasize the need for measures to ensure the safety and clinical relevance of the results obtained by machine learning, and to remember than computer-generated algorithms cannot yet replace the trained human eye.

"These are tools that not only may help the decision-making process but also find patterns that might not be evident in analysis of smaller data sets or anecdotal experience," Dr. Kanevsky and coauthors conclude. "By embracing machine learning, modern plastic surgeons may be able to redefine the specialty while solidifying their role as leaders at the forefront of scientific advancement in surgery."

Plastic and Reconstructive Surgery® is published by Wolters Kluwer.

Click here to read "Big Data and Machine Learning in Plastic Surgery: A New Frontier in Surgical Innovation”

Article: "Big Data and Machine Learning in Plastic Surgery: A New Frontier in Surgical Innovation” (doi: 10.1097/PRS.0000000000002088.)

About Plastic and Reconstructive Surgery
For more than 60 years, Plastic and Reconstructive Surgery® (http://journals.lww.com/plasreconsurg/) has been the one consistently excellent reference for every specialist who uses plastic surgery techniques or works in conjunction with a plastic surgeon. The official journal of the American Society of Plastic Surgeons, Plastic and Reconstructive Surgery® brings subscribers up-to-the-minute reports on the latest techniques and follow-up for all areas of plastic and reconstructive surgery, including breast reconstruction, experimental studies, maxillofacial reconstruction, hand and microsurgery, burn repair, and cosmetic surgery, as well as news on medico-legal issues.

About ASPS
The American Society of Plastic Surgeons (ASPS) is the world's largest organization of board-certified plastic surgeons. Representing more than 7,000 Member Surgeons, the Society is recognized as a leading authority and information source on aesthetic and reconstructive plastic surgery. ASPS comprises more than 94 percent of all board-certified plastic surgeons in the United States. Founded in 1931, the Society represents physicians certified by The American Board of Plastic Surgery or The Royal College of Physicians and Surgeons of Canada. ASPS advances quality care to plastic surgery patients by encouraging high standards of training, ethics, physician practice and research in plastic surgery. You can learn more and visit the American Society of Plastic Surgeons at www.plasticsurgery.org or www.facebook.com/PlasticSurgeryASPS and www.twitter.com/ASPS_news.

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