Rana Khizar, Beecher Mark, Caltabiano Carmelo, Macri Carmelo, Zhao Yang, Verjans Johan, Selva Dinesh
Department of Ophthalmology & Visual Sciences, South Australian Institute of Ophthalmology, University of Adelaide, North Terrace, SA 5000, Australia.
Australian Institute for Machine Learning, The University of Adelaide, SA 5000, Adelaide, Australia.
Eur J Ophthalmol. 2025 Jan;35(1):346-351. doi: 10.1177/11206721241249773. Epub 2024 May 6.
To develop and validate a deep learning facial landmark detection network to automate the assessment of periocular anthropometric measurements.
Patients presenting to the ophthalmology clinic were prospectively enrolled and had their images taken using a standardised protocol. Facial landmarks were segmented on the images to enable calculation of marginal reflex distance (MRD) 1 and 2, palpebral fissure height (PFH), inner intercanthal distance (IICD), outer intercanthal distance (OICD), interpupillary distance (IPD) and horizontal palpebral aperture (HPA). These manual segmentations were used to train a machine learning algorithm to automatically detect facial landmarks and calculate these measurements. The main outcomes were the mean absolute error and intraclass correlation coefficient.
A total of 958 eyes from 479 participants were included. The testing set consisted of 290 eyes from 145 patients. The AI algorithm demonstrated close agreement with human measurements, with mean absolute errors ranging from 0.22 mm for IPD to 0.88 mm for IICD. The intraclass correlation coefficients indicated excellent reliability (ICC > 0.90) for MRD1, MRD2, PFH, OICD, IICD, and IPD, while HPA showed good reliability (ICC 0.84). The landmark detection model was highly accurate and achieved a mean error rate of 0.51% and failure rate at 0.1 of 0%.
The automated facial landmark detection network provided accurate and reliable periocular measurements. This may help increase the objectivity of periocular measurements in the clinic and may facilitate remote assessment of patients with tele-health.
开发并验证一种深度学习面部地标检测网络，以实现眼周人体测量评估的自动化。
前瞻性纳入眼科门诊患者，并按照标准化方案为其拍摄图像。在图像上分割面部地标，以便计算边缘反射距离（MRD）1和2、睑裂高度（PFH）、内眦间距（IICD）、外眦间距（OICD）、瞳孔间距（IPD）和水平睑裂孔径（HPA）。这些手动分割用于训练机器学习算法，以自动检测面部地标并计算这些测量值。主要结果是平均绝对误差和组内相关系数。
共纳入479名参与者的958只眼睛。测试集包括145名患者的290只眼睛。人工智能算法与人工测量结果显示出高度一致性，平均绝对误差范围从IPD的0.22毫米到IICD的0.88毫米。组内相关系数表明，MRD1、MRD2、PFH、OICD、IICD和IPD具有出色的可靠性（ICC>0.90），而HPA显示出良好的可靠性（ICC 0.84）。地标检测模型高度准确，平均错误率为0.51%，失败率为0%。
自动面部地标检测网络提供了准确可靠的眼周测量结果。这可能有助于提高临床眼周测量的客观性，并可能促进远程医疗对患者的评估。
