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Machine Learning, Deep Learning, Artificial Intelligence and Aesthetic Plastic Surgery: A Qualitative Systematic
Raquel Nogueira1, Marina Eguchi2, Julia Kasmirski3
1Department of Surgery, Montefiore Medical Center, 1825 Eastchester Rd, Bronx, NY, 10461, USA. raquelnogueiramd@gmail.com.
Aesthetic Plastic Surgery
|October 9, 2024
Summary
Machine learning (ML), deep learning (DL), and artificial intelligence (AI) show great potential in aesthetic plastic surgery. These technologies can optimize treatments and predict complications, but careful use is needed to manage patient expectations.
Area of Science:
- Medical Informatics
- Computer-Aided Surgery
- Plastic Surgery
Background:
- The integration of advanced computational techniques is rapidly evolving within various medical disciplines.
- Aesthetic plastic surgery is increasingly exploring innovative tools to enhance patient outcomes and surgical precision.
Purpose of the Study:
- To systematically review the application of machine learning (ML), deep learning (DL), and artificial intelligence (AI) in aesthetic plastic surgery.
- To assess the current landscape and potential impact of these technologies on surgical decision-making and complication prediction.
Main Methods:
- A qualitative systematic review was conducted following PRISMA guidelines.
- Searches were performed on MEDLINE/PubMed, EMBASE, and Cochrane Library using terms related to ML, DL, AI, and plastic surgery.
- The ROBINS-I tool was used for risk-of-bias assessment of included non-randomized studies.
Main Results:
- Eighteen studies published between 2019 and 2024 met the inclusion criteria.
- Applications spanned various procedures including breast augmentation, rhinoplasty, facial rejuvenation, and body contouring.
- Image-based AI, ML, and DL algorithms were utilized to improve decision-making and identify factors influencing postoperative complications.
Conclusions:
- AI, ML, and DL algorithms hold significant promise for revolutionizing aesthetic plastic surgery.
- These technologies can aid in optimizing treatment plans, predicting complications, and clarifying patient concerns.
- Careful implementation is crucial to avoid setting unrealistic patient expectations, emphasizing the continued importance of conservative surgical communication.
