Publications by authors named "Ben H Zhang"

Background: Osseous genioplasty is a powerful procedure that can correct chin dysmorphology; however, traditional techniques may result in chin ptosis or a "witch's chin" deformity. Iatrogenic chin ptosis is thought to be caused by excessive degloving of soft tissue with a failure to reattach the mentalis muscle. In the authors' study, they compared the "no-degloving" technique (using a 90-degree plate with lag-screw fixation) to the "traditional" technique, for minimization of chin ptosis.

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Background: Patients desire face-lifting procedures primarily to appear younger, more refreshed, and attractive. Because there are few objective studies assessing the success of face-lift surgery, the authors used artificial intelligence, in the form of convolutional neural network algorithms alongside FACE-Q patient-reported outcomes, to evaluate perceived age reduction and patient satisfaction following face-lift surgery.

Methods: Standardized preoperative and postoperative (1 year) images of 50 consecutive patients who underwent face-lift procedures (platysmaplasty, superficial musculoaponeurotic system-ectomy, cheek minimal access cranial suspension malar lift, or fat grafting) were used by four neural networks (trained to identify age based on facial features) to estimate age reduction after surgery.

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Breast implant removal and replacement has been a common secondary breast procedure in the long-term maintenance of breast augmentation, but more recently growing concerns about silicone-related systemic illness, breast implant-associated anaplastic large cell lymphoma (BIA-ALCL), and changing perceptions of aesthetic beauty have seen breast implant removal without replacement become increasingly requested by patients. Explantation can be challenging, especially when performed with a total capsulectomy. Currently, there is no evidence regarding whether a partial or total capsulectomy has any effect on BIA-ALCL risk mitigation in patients that have textured implants without disease.

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Background: Male-to-female transgender patients desire to be identified, and treated, as female, in public and social settings. Facial feminization surgery entails a combination of highly visible changes in facial features. To study the effectiveness of facial feminization surgery, we investigated preoperative/postoperative gender-typing using facial recognition neural networks.

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