Three-dimensional medical printing and associated legal issues in plastic surgery: a scoping review

Three-dimensional printing (3DP) represents an emerging field of surgery. 3DP can facilitate the plastic surgeon’s workflow, including preoperative planning, intraoperative assistance, and postoperative follow-up. The broad clinical application spectrum stands in contrast to the paucity of research on the legal framework of 3DP. This imbalance poses a potential risk for medical malpractice lawsuits. To address this knowledge gap, we aimed to summarize the current body of legal literature on medical 3DP in the US legal system. By combining the promising clinical use of 3DP with its current legal regulations, plastic surgeons can enhance patient safety and outcomes

Three-dimensional Medical Printing and Associated Legal Issues in Plastic Surgery: A Scoping Review

Three-dimensional printing (3DP) represents an emerging field of surgery. 3DP can facilitate the plastic surgeon’s workflow, including preoperative planning, intraoperative assistance, and postoperative follow-up. The broad clinical application spectrum stands in contrast to the paucity of research on the legal framework of 3DP. This imbalance poses a potential risk for medical malpractice lawsuits. To address this knowledge gap, we aimed to summarize the current body of legal literature on medical 3DP in the US legal system. By combining the promising clinical use of 3DP with its current legal regulations, plastic surgeons can enhance patient safety and outcomes

Towards a Reliable and Rapid Automated Grading System in Facial Palsy Patients: Facial Palsy Surgery Meets Computer Science

Background: Reliable, time- and cost-effective, and clinician-friendly diagnostic tools are cornerstones in facial palsy (FP) patient management. Different automated FP grading systems have been developed but revealed persisting downsides such as insufficient accuracy and cost-intensive hardware. We aimed to overcome these barriers and programmed an automated grading system for FP patients utilizing the House and Brackmann scale (HBS). Methods: Image datasets of 86 patients seen at the Department of Plastic, Hand, and Reconstructive Surgery at the University Hospital Regensburg, Germany, between June 2017 and May 2021, were used to train the neural network and evaluate its accuracy. Nine facial poses per patient were analyzed by the algorithm. Results: The algorithm showed an accuracy of 100%. Oversampling did not result in altered outcomes, while the direct form displayed superior accuracy levels when compared to the modular classification form (n = 86; 100% vs. 99%). The Early Fusion technique was linked to improved accuracy outcomes in comparison to the Late Fusion and sequential method (n = 86; 100% vs. 96% vs. 97%). Conclusions: Our automated FP grading system combines high-level accuracy with cost- and time-effectiveness. Our algorithm may accelerate the grading process in FP patients and facilitate the FP surgeon’s workflow