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Background: The advent of artificial intelligence (AI) in microsurgery has tremendous potential in plastic and reconstructive surgery, with possibilities to elevate surgical precision, planning, and patient outcomes. This systematic review seeks to summarize available studies on the implementation of AI in microsurgery and classify these into subdomains where AI can revolutionize our field. Methods: Adhering to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a meticulous search strategy was used across multiple databases. The inclusion criteria encompassed articles that explicitly discussed AI’s integration in microsurgical practices. Our aim was to analyze and classify these studies across subdomains for future development. Results: The search yielded 2377 articles, with 571 abstracts eligible for screening. After shortlisting and reviewing 86 full-text articles, 29 studies met inclusion criteria. Detailed analysis led to the classification of 6 subdomains within AI applications in microsurgery, including information and knowledge delivery, microsurgical skills training, preoperative planning, intraoperative navigational aids and automated surgical tool control, flap monitoring, and postoperative predictive analytics for patient outcomes. Each subtheme showcased the multifaceted impact of AI on enhancing microsurgical procedures, from preoperative planning to postoperative recovery. Conclusions: The integration of AI into microsurgery signals a new dawn of surgical innovation, albeit with the caution warranted by its nascent stage and application diversity. The authors present a systematic review and 6 clear subdomains across which AI will likely play a role within microsurgery. Continuous research, ethical diligence, and cross-disciplinary cooperation is necessary for its successful integration within our specialty.

Background: Revisional surgery for aesthetic breast augmentation remains a challenging procedure. Polyurethane (PU) implants have been found to avoid capsular contracture recurrence as well as to prevent implant displacement by bio-integrating with the pocket. Objectives: Our study aimed to assess the use of PU implants in breast revisional surgery and to provide an algorithm. Methods: Over a 5-year period, a prospective study was conducted involving consecutive patients undergoing implant revision. Patient demographics, previous breast procedures, and specific surgical details were documented. Postoperative outcomes were followed up. Results: Out of 92 patients (184 breasts), 78 (156 breasts) were included in the analysis. The average age was 47.5, with a BMI of 22.3 and a mean follow-up of 5 years. A majority (63%) represented secondary revisional cases, while 37% were tertiary cases. Implant size averaged 296 cc, with 53% placed in retropectoral position and 47% prepectoral. Significantly more implants in secondary cases were changed from prepectoral to retropectoral (P = .005), and in tertiary changed from retropectoral to prepectoral (P = .002). Complete capsulectomy was performed in 61.5% and partial in 25.6%. Additional lipofilling was performed in 32%, and concurrent mastopexy in 40%. Revisional surgery in our series had a 1.9% acute complication rate, 4.5% longer-term reoperation rate for corrections, 0.6% implant exchange rate, and no recurrent capsular contracture. Conclusions: This is the first study to provide data on outcomes of revisional breast augmentation surgery with PU implants. It shows that polyurethane implants offer consistent stability and have low rates of recurrent capsular contracture in revisional surgery. Level of Evidence: 3:

Three-dimensional imaging technologies are increasingly used in breast reconstructive and plastic surgery due to their potential for efficient and accurate preoperative assessment and planning. This study systematically evaluates the accuracy and consistency of six commercially available 3D scanning applications (apps)—Structure Sensor, 3D Scanner App, Heges, Polycam, SureScan, and Kiri—in reconstructing the female torso. To avoid variability introduced by human subjects, a silicone breast mannequin model was scanned, with fiducial markers placed at known anatomical landmarks. Manual distance measurements were obtained using calipers by two independent evaluators and compared to digital measurements extracted from 3D reconstructions in Blender software. Each scan was repeated six times per application to ensure reliability. SureScan demonstrated the lowest mean error (2.9 mm), followed by Structure Sensor (3.0 mm), Heges (3.6 mm), 3D Scanner App (4.4 mm), Kiri (5.0 mm), and Polycam (21.4 mm), which showed the highest error and variability. Even the app using an external depth sensor (Structure Sensor) showed no statistically significant accuracy advantage over those using only the iPad’s built-in camera (except for Polycam), underscoring that software is the primary driver of performance, not hardware (alone). This work provides practical insights for selecting mobile 3D scanning tools in clinical workflows and highlights key limitations, such as scaling errors and alignment artifacts. Future work should include patient-based validation and explore deep learning to enhance reconstruction quality. Ultimately, this study lays the foundation for more accessible and cost-effective 3D imaging in surgical practice, showing that smartphone-based tools can produce clinically useful scans.