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A Novel Artificial Intelligence-assisted Risk Assessment Model for Preventing Complications in Esthetic Surgery
1Bukret Esthetic Surgery, Buenos Aires, Argentina.
Plastic and Reconstructive Surgery. Global Open
|August 23, 2021
Summary
Plastic surgery complications can be predicted using artificial intelligence. Key risk factors include body mass index, age, and Caprini score, enabling better patient eligibility and complication prevention.
Area of Science:
- Plastic Surgery
- Artificial Intelligence in Medicine
- Risk Assessment
Background:
- Preventing complications is crucial for plastic surgeons to reduce morbidity, mortality, and enhance patient satisfaction.
- Identifying predictive risk factors for complications is essential for improving surgical outcomes.
Purpose of the Study:
- To evaluate predictive risk factors for complications in esthetic surgery.
- To validate a novel artificial intelligence-based risk assessment model for predicting complications.
Main Methods:
- Retrospective review of 372 esthetic surgery procedures (2015-2020).
- Pearson correlation and one-way ANOVA used for risk factor analysis.
- Machine learning (support vector machine) validated the risk scoring model.
Main Results:
- Complications occurred in 7.5% of patients.
- Body mass index, age, and Caprini score (≥5) were significant risk factors (P < 0.01).
- The AI model achieved 100% accuracy on training data and 97.3% on test data.
Conclusions:
- Body mass index, age, and Caprini score are significant predictors of complications in esthetic surgery.
- The AI-driven risk assessment system effectively identifies patients at risk and aids in complication prevention.
