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Artificial intelligence (AI) is revolutionizing plastic surgery through its remarkable advancements in various domains such as image analysis, robotic assistance, predictive analytics, and augmented reality. Predictive analytics, powered by AI, harnesses patient data to predict surgical outcomes, minimize risks, and tailor treatment plans, thereby optimizing patient care and safety. Augmented reality and virtual reality technology are also reshaping the cosmetic surgery landscape, providing immersive experiences for preoperative imaging, intraoperative guidance, and advanced skills through simulation. Looking ahead, the future of AI in plastic surgery holds great promise, including personalized medicine, bioprinting of tissues and organs, and continuous learning through iterative improvement algorithms based on real-world surgical experience. However, amid these transformational advances, ethical considerations and regulatory frameworks must evolve to ensure the responsible deployment of AI, protect patient privacy, minimize errors and algorithmic deviation, and uphold standards of fairness and transparency. Our study aims to explore the role of AI in the field of plastic surgery with the potential for the future in mind. In summary, AI is considered a beacon of innovation in plastic surgery, enhancing surgical precision, enhancing patient outcomes, and heralding a future where interventions rely on personalized technology that will redefine the boundaries of aesthetic and regenerative medicine. [ABSTRACT FROM AUTHOR]

Background: Timely diagnosis of meniscus injuries is key for preventing knee joint dysfunction and improving patient outcomes because it decreases morbidity and facilitates treatment planning. Purpose: To train and evaluate a deep learning model for automated detection of meniscus tears on knee magnetic resonance imaging (MRI). Study type: Bicentric retrospective study. Subjects: In total, 584 knee MRI studies, divided among training (n = 234), testing (n = 200), and external validation (n = 150) data sets, were used in this study. The public data set MRNet was used as a second external validation data set to evaluate the performance of the model. Sequence: A 3 T, coronal, and sagittal images from T1‐weighted proton density (PD) fast spin‐echo (FSE) with fat saturation and T2‐weighted FSE with fat saturation sequences. Assessment: The detection system for meniscus tear was based on the improved YOLOv4 model with Darknet‐53 as the backbone. The performance of the model was also compared with that of three radiologists of varying levels of experience. The determination of the presence of a meniscus tear from surgery reports was used as the ground truth for the images. Statistical Tests: Sensitivity, specificity, prevalence, positive predictive value, negative predictive value, accuracy, and receiver operating characteristic curve were used to evaluate the performance of the detection model. Two‐way analysis of variance, Wilcoxon signed‐rank test, and Tukey's multiple tests were used to evaluate differences in performance between the model and radiologists. Results: The overall accuracies for detecting meniscus tears using our model on the internal testing, internal validation, and external validation data sets were 95.4%, 95.8%, and 78.8%, respectively. One radiologist had significantly lower performance than our model in detecting meniscal tears (accuracy: 0.9025 ± 0.093 vs. 0.9580 ± 0.025). Data Conclusion: The proposed model had high sensitivity, specificity, and accuracy for detecting meniscus tears on knee MRIs. Evidence Level: 3 Technical Efficacy: Stage 2 [ABSTRACT FROM AUTHOR]

This study evaluated the efficacy of machine learning and radiomics of preoperative multiparameter MRIs in predicting low- vs high-risk histopathologic features and early vs advanced FIGO stage (IA vs IB or higher) in endometrial cancer. This retrospective study of patients with endometrial cancer histologically confirmed from 2008 through 2023 excluded those with: (a) previous treatment for endometrial carcinoma, (b) incomplete MRI examinations or low-quality MR images, (c) incomplete pathology reports, (d) non-visualized tumors on MRI, or (e) distant metastases. In total, 110 radiomic features were extracted using commercial PACS built-in software following segmentation after sagittal T2-weighted imaging (T2WI), contrast enhanced T1-weighted imaging (CE-T1WI), and diffusion weighted imaging (DWI). The radiomic features from each imaging sequence were utilized for initial modeling. A combined model, which included features retained from all 3 sequences, was then established. The area under the receiver operating characteristic curve (AUC) determined the efficacy of each model. For 5 specific histopathologic features, the combined model achieved AUCs of 0.87 (95% CI, 0.85-0.90), 0.90 (95% CI, 0.88-0.92), 0.88 (95% CI, 0.87-0.90), 0.88 (95% CI, 0.86-0.92), and 0.87 (95% CI, 0.86-0.90). This model incorporated 38 radiomic features: 12 from T2WI, 17 from CE-T1WI, and 9 from DWI. In conclusion, an MRI radiomics-based model can differentiate between early- and advanced-stage endometrial cancer and between low- and high-risk histologic markers, giving it the potential to predict high risk and stratify preoperative risk in those with endometrial cancer. The findings may aid personalized preoperative assessments to guide clinical decision-making in endometrial cancer., Competing Interests: C.H. Liang is an employee of Herricane Medical Care Company in Taiwan. No funding was received from the Herricane Medical Care Company for this research or publication activities., (AJCR Copyright © 2024.)