Peterson Jeffrey C, Nahass George R, Lasalle Claudia, Bradley Deanna C, Wu David, Zorra Isabella, Nguyen Alvin, Choudhary Akriti, Heinze Kevin, Purnell Chad A, Setabutr Pete, Yi Darvin, Tran Ann Q
Illinois Eye and Ear Infirmary, Department of Ophthalmology, University of Illinois at Chicago, Chicago, Illinois.
College of Medicine, University of Illinois at Chicago, Chicago, Illinois.
Ophthalmol Sci. 2025 Jul 18;5(6):100887. doi: 10.1016/j.xops.2025.100887. eCollection 2025 Nov-Dec.
To validate a custom FIJI (ImageJ) program for more reproducible, faster curvilinear periorbital measurements, as compared with 2 custom artificial intelligence-based tools.
Combined technical validation and method comparison study.
Front-facing photographs of 45 cleft palate syndromic patients.
A FIJI (ImageJ)-based macro script tool, OrbitJ (semiautomated), was developed, requiring 15 user input steps to generate 38 measurements per photo. The user outlines the irises to set image scale and then selects points along the lid margins and brow lines. Linear interpolation and fourth-degree polynomial fit lines were used to generate periorbital measurements. This tool was compared against our previously developed deep learning algorithm for periorbital measurements, OrbitMap (automated), another open-source algorithm PeriOrbitAI (automated), and against manual measurements. Four human graders measured 45 photos once both manually and with OrbitJ. Intrarater and interrater measurements were performed with 10 photos manually, 3 in triplicate manually and 5 in triplicate with OrbitJ. Fourteen manual measurements were performed: time per image, iris diameter, margin reflex distance (MRD) 1 and 2, inferior scleral show (ISS), medial, central, and lateral brow height, canthal tilt, vertical dystopia, interpupillary distance, and inner and outer canthal distance (OCD).
The mean absolute error, reliability, bias, and Pearson correlation of periorbital measurements.
Analysis was successful in all 45 images for all methods except PeriOrbitAI, which failed on 6 images. For manual and semiautomated intra- and interrater measurements, reliability was considered moderate or better (intraclass correlation coefficient [ICC] >0.5) for all measurements excluding elapsed time. Manual interrater mean absolute error was <1 mm all measures except OCD. Reliability and correlation were high (ICC, Pearson correlation coefficient >0.8) between all OrbitJ and manual measurements. Comparing OrbitMap to manual measurements, ICC and Pearson correlation coefficient were >0.5 except for ISS, borderline for OCD (ICC = 0.51). Reliability between PeriOrbitAI and manual measurements was low (ICC <0.5) except for MRD2 and OCD, and correlation was moderate (Pearson correlation coefficient = 0.49-0.75). The mean analysis time per image was 13.4 ± 4 minutes (manual measurements), 5.4 ± 1.9 minutes (semiautomated OrbitJ), 10.71 ± 1.65 seconds (automated PeriOrbitAI), and 1.45 ± 0.15 seconds (automated OrbitMap) ( < 0.001).
Compared with manual measurements, all semiautomated OrbitJ measurements and most automated OrbitMap measurements were reliable. Notably, only 2 PeriOrbitAI measurements were reliable. All methods demonstrated significant time savings over manual measurements.
Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
验证一个自定义的FIJI(ImageJ)程序,以实现比2种基于人工智能的自定义工具更可重复、更快的眶周曲线测量。
联合技术验证和方法比较研究。
45例腭裂综合征患者的正面照片。
开发了一种基于FIJI(ImageJ)的宏脚本工具OrbitJ(半自动),每张照片需要15个用户输入步骤来生成38项测量数据。用户勾勒出虹膜以设置图像比例,然后沿着眼睑边缘和眉线选择点。使用线性插值和四次多项式拟合线来生成眶周测量数据。将该工具与我们之前开发的用于眶周测量的深度学习算法OrbitMap(自动)、另一种开源算法PeriOrbitAI(自动)以及手动测量进行比较。4名评分者分别用手动和OrbitJ测量45张照片各一次。对10张照片进行评分者内和评分者间测量,其中3张照片手动测量3次,5张照片用OrbitJ测量3次。进行了14项手动测量:每张图像的时间、虹膜直径、边缘反射距离(MRD)1和2、下巩膜显露(ISS)、内侧、中央和外侧眉高、眦倾斜度、垂直错位、瞳孔间距以及内眦和外眦距离(OCD)。
眶周测量的平均绝对误差、可靠性、偏差和Pearson相关性。
除PeriOrbitAI在6张图像上失败外,所有方法在全部45张图像上的分析均成功。对于手动和半自动评分者内和评分者间测量,除了所用时间外,所有测量的可靠性均被认为是中等或更好(组内相关系数[ICC]>0.5)。手动评分者间平均绝对误差除OCD外所有测量均<1 mm。所有OrbitJ测量与手动测量之间的可靠性和相关性都很高(ICC、Pearson相关系数>0.8)。将OrbitMap与手动测量进行比较,除ISS外ICC和Pearson相关系数>0.5,OCD处于临界值(ICC = 0.51)。PeriOrbitAI与手动测量之间的可靠性较低(ICC<0.5),除了MRD2和OCD,相关性为中等(Pearson相关系数 = 0.49 - 0.75)。每张图像的平均分析时间为13.4±4分钟(手动测量)、5.4±1.9分钟(半自动OrbitJ)、10.71±1.65秒(自动PeriOrbitAI)和1.45±0.15秒(自动OrbitMap)(P<0.001)。
与手动测量相比,所有半自动OrbitJ测量和大多数自动OrbitMap测量都是可靠的。值得注意的是,只有2项PeriOrbitAI测量是可靠的。所有方法与手动测量相比均显著节省了时间。
在本文末尾的脚注和披露中可能会找到专有或商业披露信息。