Osseous Genioplasty: Prevention of Witch's Chin Deformity with No-Degloving Technique.

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. METHODS: The authors compared genioplasty techniques for consecutive patients: group 1 (traditional) underwent degloving for fixation of a stair-step plate, whereas group 2 (no-degloving) underwent lag-screw fixation (n = 50; 25 patients per group). The authors compared operating room time, advancement, complications, preoperative-to-postoperative vertical height change of the pogonion and menton (obtained through cone beam computed tomographic scans), surgeons' assessment of witch's chin, and FACE-Q surveys. RESULTS: No-degloving versus traditional groups had similar age and sex distributions, horizontal/vertical change (5 mm/2 mm versus 6 mm/2 mm), length of surgery, and complication rate (5 percent). The traditional group had more deviation from expected position for both the pogonion (3.4 mm versus 1.2 mm; p ≤ 0.05) and menton (2.9 mm versus 0.8 mm; p ≤ 0.05), and more occurrences of witch's chin (six versus zero). No-degloving was superior for several FACE-Q scales, including Chin Appearance, Quality of Life, Satisfaction with Decision to Undergo Procedure, and Satisfaction with Outcome. CONCLUSION: No-degloving osseous genioplasty is a safe, reproducible technique that results in decreased soft-tissue ptosis and increased patient satisfaction. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, III.

Turning Back the Clock: Artificial Intelligence Recognition of Age Reduction after Face-Lift Surgery Correlates with Patient Satisfaction.

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. In addition, FACE-Q surveys were used to measure patient-reported facial aesthetic outcome. Patient satisfaction was compared to age reduction. RESULTS: The neural network preoperative age accuracy score demonstrated that all four neural networks were accurate in identifying ages (mean score, 100.8). Patient self-appraisal age reduction reported a greater age reduction than neural network age reduction after a face lift (-6.7 years versus -4.3 years). FACE-Q scores demonstrated a high level of patient satisfaction for facial appearance (75.1 ± 8.1), quality of life (82.4 ± 8.3), and satisfaction with outcome (79.0 ± 6.3). Finally, there was a positive correlation between neural network age reduction and patient satisfaction. CONCLUSION: Artificial intelligence algorithms can reliably estimate the reduction in apparent age after face-lift surgery; this estimated age reduction correlates with patient satisfaction. CLINICAL QUESTION/LEVEL OF EVIDENCE: Diagnostic, IV.

Not All Breast Explants Are Equal: Contemporary Strategies in Breast Explantation Surgery.

SUMMARY: 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. Total capsulectomy with incomplete resection of a mass can contribute to hyperprogression of BIA-ALCL and death. There have also been cases of BIA-ALCL diagnosed years after removal of the textured device and "total capsulectomy." Therefore, the common practice of simple prophylactic capsulectomy in a textured implant to mitigate future disease has not been established and at the current time should be discouraged. In addition, aesthetic outcomes can be quite variable, and patients should have appropriate preoperative counseling regarding the indications and contraindications for explantation, associated risks, financial implications, and postoperative appearance. The authors review salient aspects related to the planning and management of breast implant removal.

Facial Recognition Neural Networks Confirm Success of Facial Feminization Surgery.

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. METHODS: In this study, standardized frontal and lateral view preoperative and postoperative images of 20 male-to-female patients who completed hard- and soft-tissue facial feminization surgery procedures were used, along with control images of unoperated cisgender men and women (n = 120 images). Four public neural networks trained to identify gender based on facial features analyzed the images. Correct gender-typing, improvement in gender-typing (preoperatively to postoperatively), and confidence in femininity were analyzed. RESULTS: Cisgender male and female control frontal images were correctly identified 100 percent and 98 percent of the time, respectively. Preoperative facial feminization surgery images were misgendered 47 percent of the time (recognized as male) and only correctly identified as female 53 percent of the time. Postoperative facial feminization surgery images were gendered correctly 98 percent of the time; this was an improvement of 45 percent. Confidence in femininity also improved from a mean score of 0.27 before facial feminization surgery to 0.87 after facial feminization surgery. CONCLUSIONS: In the first study of its kind, facial recognition neural networks showed improved gender-typing of transgender women from preoperative facial feminization surgery to postoperative facial feminization surgery. This demonstrated the effectiveness of facial feminization surgery by artificial intelligence methods. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, IV.