Ein Liliana, Trzcinski Lauren, Perry Luke, Bark Kee Yoon, Hadlock Tessa, Guarin Diego L
Department of Otolaryngology-Head and Neck Surgery, Division of Facial Plastic and Reconstructive Surgery, University of Miami Miller School of Medicine, Miami, Florida, USA.
Department of Otolaryngology, Division of Facial Plastic and Reconstructive Surgery, Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts, USA.
Facial Plast Surg Aesthet Med. 2023 Sep-Oct;25(5):409-414. doi: 10.1089/fpsam.2022.0235. Epub 2023 Mar 1.
The nasolabial fold (NLF) greatly contributes to facial aesthetics; changes to NLF depth and vector are disfiguring in patients with facial paralysis (FP). NLF parameters are integral to clinician-graded outcomes, but automated programs currently lack NLF identification capabilities. To incorporate an automated NLF identification and quantification function into the facial landmark program, Emotrics, and to compare new Emotrics-derived NLF data to clinician-graded electronic facial paralysis assessment (eFACE) data for accuracy. Photographs of 135 patients with FP were marked bilaterally, using identification markers manually placed on each NLF. A machine learning model was trained to automatically localize the markers using these data. Once Emotrics accurately identified the NLF and its corresponding vector, photographs of 20 additional patients who underwent facial reanimation procedures were assessed by the algorithm. The enhanced Emotrics algorithm successfully identified the NLF, and measured the vector from midline, in a series of patients with FP. NLF vector data closely matched corresponding eFACE parameters. Furthermore, changes in NLF presence and vector were detected following facial reanimation procedures. The Emotrics program now provides critical NLF data, providing objective parameters for clinicians interested in changing NLF dynamics after FP.
鼻唇沟（NLF）对面部美学有很大贡献；面部瘫痪（FP）患者鼻唇沟深度和走向的改变会导致面容毁损。鼻唇沟参数是临床医生分级结果的重要组成部分，但目前的自动化程序缺乏识别鼻唇沟的能力。为了将自动识别和量化鼻唇沟的功能纳入面部标志点程序Emotrics，并将新的Emotrics得出的鼻唇沟数据与临床医生分级的电子面部瘫痪评估（eFACE）数据进行准确性比较。对135例面部瘫痪患者的照片进行双侧标记，在每条鼻唇沟上手动放置识别标记。使用这些数据训练机器学习模型以自动定位标记。一旦Emotrics准确识别出鼻唇沟及其相应走向，算法就会对另外20例接受面部重建手术的患者的照片进行评估。增强后的Emotrics算法成功识别出鼻唇沟，并测量了一系列面部瘫痪患者从鼻唇沟到中线的走向。鼻唇沟走向数据与相应的eFACE参数密切匹配。此外，在面部重建手术后检测到了鼻唇沟存在情况和走向的变化。Emotrics程序现在提供了关键的鼻唇沟数据，为关注面部瘫痪后改变鼻唇沟动态的临床医生提供了客观参数。
