Applications and challenges of artificial intelligence in plastic surgery imaging: A narrative review

Table 1 Summary of studies on artificial intelligence applications in medical imaging for plastic surgery, detailing the surgical procedure, outcomes measured, artificial intelligence utilization, type of artificial intelligence used, and key findings

Ref.	Surgical procedure	Outcome measured	AI used for	Type of AI used	Results
Zhu et al[21], 2016	Mandibular osteotomy	Osteotomy accuracy and intraoperative deviations	Surgical navigation and osteotomy guidance	Augmented reality-based surgical navigation	Significant improvement in osteotomy precision
Qu et al[17], 2015	Distraction osteogenesis	Postoperative symmetry and distractor placement	Enhancing distractor placement accuracy	Machine learning-enhanced augmented reality	Enhanced postoperative symmetry and reduced operative time
Le et al[18], 2023	DIEP flap reconstruction	Perforator detection accuracy compared to CTA	Preoperative vascular imaging	Deep learning-based vascular mapping	High accuracy in perforator detection
Hummelink et al[19], 2019	DIEP flap breast reconstruction	Effectiveness of 3D vascular mapping	Improved intraoperative vascular mapping	3D convolutional neural networks	Reduced operative time and improved flap selection
Pereira et al[20], 2018	Perforator identification in anterolateral thigh flaps	Agreement between thermographic imaging and CTA	Validation of AI-assisted thermography	Computer vision for thermographic analysis	High correlation between AI-based and CTA imaging
Zhu et al[21], 2018	Mandibular osteotomy	Precision in osteotomy execution	Augmented reality guidance for surgery	Neural network-based surgical guidance	Improved accuracy in osteotomies
Kim et al[22], 2019	Robotic-assisted microsurgery	Enhancements in microsurgical precision	Microsurgical planning and robotic assistance	Robotic-assisted AI algorithms	Significant improvements in microsurgical execution
Brenac et al[23], 2024	Perforator flap harvest	Accuracy in perforator visualization	AI-assisted imaging for intraoperative planning	AI-powered vascular imaging	Greater accuracy in vascular visualization
Ejaz et al[24], 2024	Flap viability and perfusion assessment	Detection of ischemic areas in free flaps	Predicting flap ischemia	Sensor-based deep learning models	Early ischemia detection and improved intervention success
Avila et al[25], 2024	Postoperative wound assessment	Wound classification accuracy and prediction of healing outcomes	Postoperative monitoring and complication assessment	Convolutional neural networks	High accuracy in wound classification and healing prediction
Dhawan et al[26], 2024	AI-driven risk prediction	Postoperative risk prediction using clinical variables	Personalized risk stratification	Risk assessment models using machine learning	Improved risk prediction accuracy
Chen et al[40], 2024	Wound healing assessment	Healing time prediction based on wound morphology	Deep learning-driven wound classification	Deep learning for wound classification	AI accurately predicted wound healing times
Bukret et al[27], 2021	Aesthetic surgery risk prediction	Accuracy of AI models in predicting aesthetic surgery complications	Machine learning for surgical risk assessment	Machine learning-based risk models	Reduced complications through AI-based risk models
Borsting et al[28], 2019	Rhinoplasty outcome prediction	Predictive success of AI models in rhinoplasty outcomes	Outcome prediction in rhinoplasty	Deep learning outcome prediction models	High predictive accuracy of rhinoplasty outcomes
Farid et al[29], 2024	Breast reconstruction planning	Optimization of surgical techniques in breast reconstruction	Predictive modeling for reconstruction	Neural network predictive modeling	Enhanced decision-making in breast reconstruction planning

AI: Artificial intelligence; CTA: Computed tomography angiography.