Background: Advances in deep learning (DL) have been transformative in computer vision and natural language processing, as well as in healthcare. The authors present a novel application of DL to plastic surgery. Here, the authors describe and demonstrate the mobile deployment of a deep neural network that predicts rhinoplasty status, assess model accuracy compared to surgeons, and describe future directions for such applications in plastic surgery. Methods: A deep convolutional neural network (“RhinoNet”) was developed to classify rhinoplasty images using only pixels and rhinoplasty status labels (“before”/“after”) as inputs. RhinoNet was trained using a dataset of 22,686 before and after photos which were collected from publicly available sites. Network classification was compared to that of plastic surgery attendings and residents on 2269 previously-unseen test-set images. Results: RhinoNet correctly predicted rhinoplasty status in 85% of the test-set images. Sensitivity and specificity of model predictions were 0.840 (0.79–0.89) and 0.826 (0.77–0.88), respectively; the corresponding values for expert consensus predictions were 0.814 (0.76–0.87) and 0.867 (0.82–0.91). RhinoNet and humans performed with effectively equivalent accuracy in this classification task. Conclusion: The authors describe the development of DL applications to identify the presence of superficial surgical procedures solely from images and labels. DL is especially well suited for unstructured, high-fidelity visual and auditory data that does not lend itself to classical statistical analysis, and may be deployed as mobile applications for potentially unbridled use, so the authors expect DL to play a key role in many areas of plastic surgery.

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Integrated plastic surgery residency programs continue to be highly competitive, with overall increasing research experience, but slightly lower Step 1 scores and AOA membership than that in previous years. If the trend of decreasing independent and increasing integrated positions continues, the applicant only interested in plastic surgery may find the integrated pathway a more feasible option.

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Nonunions of proximal humerus fractures can be disabling as a result of pain, deformity and instability, and are often found in geriatric patients with poor bone quality. There are relatively few studies examining the treatment of nonunions of the proximal third of the humerus and the ideal treatment and surgical approach remains unclear. This case series reports the successful use of the anterolateral acromial approach for treatment of the symptomatic proximal third humerus nonunions in a geriatric group of patients with clear challenges as a result of patient comorbidities and bone quality.

In conclusion, we found smoking to be a risk factor for wound disruption following NAC reconstruction. We hope this study provides further evidence for smoking cessation prior to beginning the reconstructive process. DISCLAIMERACS NSQIP and the hospitals participating in the ACS NSQIP are the source of the data used herein; they have not verified and are not responsible for the statistical validity of the data analysis or the conclusions derived by the authors.

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