Thomas Peter B M, Gunasekera Chrishan D, Kang Swan, Baltrusaitis Tadas
NIHR Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, United Kingdom.
Norfolk and Norwich University Hospital NHS Foundation Trust, Colney Ln, Norwich, United Kingdom.
Plast Reconstr Surg Glob Open. 2020 Oct 27;8(10):e3089. doi: 10.1097/GOX.0000000000003089. eCollection 2020 Oct.
New artificial intelligence (AI) approaches to facial analysis show promise in the clinical evaluation of abnormal lid position. This could allow more naturalistic, quantitative, and automated assessment of lid position. The aim of this article was to determine whether OpenFace, an AI approach to real-time facial landmarking and analysis, can extract clinically useful measurements from images of patients before and after ptosis correction. Manual and AI-automated approaches to vertical palpebral aperture measurement of 128 eyes in pre- and postoperative full-face images of ptosis patients were compared in this study. Agreement in interpupillary distance to vertical palpebral aperture ratio between clinicians and an AI-based system was assessed. Image quality varied highly with interpupillary distance defined by a mean of 143.4 pixels (min = 60, max = 328, SD = 80.3 pixels). A Bland-Altman analysis suggests a good agreement between manual and AI analysis of vertical palpebral aperture (94.4% of measurements falling within 2 SDs of the mean). Correlation between the 2 methods yielded a Pearson's r(126) = 0.87 ( < 0.01) and r = 0.76. This feasibility study suggests that existing, open-source approaches to facial analysis can be applied to the clinical assessment of patients with abnormal lid position. The approach could be extended to further quantify clinical assessment of oculoplastic conditions.
新的人工智能（AI）面部分析方法在异常眼睑位置的临床评估中显示出前景。这可以实现对眼睑位置更自然、定量和自动化的评估。本文的目的是确定OpenFace（一种用于实时面部 landmarking 和分析的人工智能方法）能否从睑下垂矫正前后患者的图像中提取临床有用的测量值。本研究比较了睑下垂患者术前和术后全脸图像中128只眼睛垂直睑裂孔径测量的手动和人工智能自动化方法。评估了临床医生和基于人工智能的系统在瞳孔间距与垂直睑裂孔径比值方面的一致性。图像质量差异很大，瞳孔间距的平均值为143.4像素（最小值 = 60，最大值 = 328，标准差 = 80.3像素）。Bland-Altman分析表明，手动和人工智能对垂直睑裂孔径的分析之间具有良好的一致性（94.4%的测量值落在平均值的2个标准差范围内）。两种方法之间的相关性产生Pearson's r(126) = 0.87（<0.01）和r = 0.76。这项可行性研究表明，现有的开源面部分析方法可应用于异常眼睑位置患者的临床评估。该方法可扩展以进一步量化眼部整形疾病的临床评估。
