Heisinger, Stephan

Abstract Background Our understanding of the functional anatomy of the face is constantly improving. To date, it is unclear whether the anatomic location of the line of ligaments has any functional importance during normal facial movements such as smiling. Objectives The authors sought to identify differences in facial movements between the medial and lateral midface by means of skin vector displacement analyses derived from 3-dimensional (3D) imaging and to further ascertain whether the line of ligaments has both a structural and functional significance in these movements. Methods The study sample consisted of 21 healthy volunteers (9 females and 12 males) of Caucasian ethnic background with a mean age of 30.6 (8.3) years and mean BMI of 22.57 (2.5) kg/m2. 3D images of the volunteers’ faces in repose and during smiling (Duchenne type) were taken. 3D imaging-based skin vector displacement analyses were conducted. Results The mean horizontal skin displacement was 0.08 (2.0) mm in the medial midface (lateral movement) and was −0.08 (1.96) mm in the lateral midface (medial movement) (P = 0.711). The mean vertical skin displacement (cranial movement of skin toward the forehead/temple) was 6.68 (2.4) mm in the medial midface, whereas it was 5.20 (2.07) mm in the lateral midface (P = 0.003). Conclusions The results of this study provide objective evidence for an antagonistic skin movement between the medial and lateral midface. The functional boundary identified by 3D imaging corresponds to the anatomic location of the line of ligaments. Level of Evidence: 4

AbstractBackgroundDifferences in the effectiveness of neuromodulator treatments for horizontal forehead lines dependent on depth of product administration have been described. However, knowledge in respect to the fascial anatomy of the forehead still remains elusive.AimsTo relate the fascial anatomy of the forehead to the effectiveness of neuromodulator treatments by conducting a clinical, prospective, interventional split‐face study in which injections for the treatment of horizontal forehead lines are performed differently between facial sides.MethodsThis study included a total of n = 14 patients with a mean age of 35.71 (7.8) years and mean body mass index of 21.9 (3.0) kg/m2. One side of the forehead was injected superficially by positioning the product in the superficial fatty layer, whereas the contralateral side was injected deep targeting the supraperiosteal plane (random selection). The treatment outcome was rated by the physician and by two independent observers according to a forehead line severity scale (0‐4) at 14 and at 30 days.ResultsAll three observers agreed in their ratings (ICC: 0.942) that the deep injection technique resulted in a superior outcome: D14 (superficial vs deep) 0.17 (0.4) vs 0.14 (0.4; P = .583) at rest and 1.26 (0.6) vs 0.43 (0.5; P < .001) for frontalis contraction; D30 0.17 (0.4) vs 0.14 (0.3) at rest (P = .583) and 1.21 (0.6) vs 0.43 (0.5; P < .001) for frontalis contraction.ConclusionThe results of this study underscore how detailed anatomic knowledge can enhance results of aesthetic interventions, in this case horizontal forehead line treatment with neuromodulators.

The aim of the study was to longitudinally investigate symptomatic and structural factors prior to total knee replacement (TKR) surgery in order to identify influential factors that can predict a patient’s need for TKR surgery. In total, 165 participants (60% females; 64.5 ± 8.4 years; 29.7 ± 4.7 kg/m2) receiving a TKR in any of both knees within a four-year period were analyzed. Radiographic change, knee pain, knee function and quality of life were annually assessed prior to the TKR procedure. Self-learning artificial neural networks were applied to identify driving factors for the surgical procedure. Significant worsening of radiographic structural change was observed prior to TKR (p ≤ 0.0046), whereas knee symptoms (pain, function, quality of life) worsened significantly only in the year prior to the TKR procedure. By using our prediction model, we were able to predict correctly 80% of the classified individuals to undergo TKR surgery with a positive predictive value of 84% and a negative predictive value of 73%. Our prediction model offers the opportunity to assess a patient’s need for TKR surgery two years in advance based on easily available patient data and could therefore be used in a primary care setting.