Validation of a portable three-dimensional imaging system for volumetric measurement in the periorbital region
Year: 2025
Type: article
Abstract: Objectives To evaluate the reliability and accuracy of a portable structured light-based three-dimensional (3D) imaging system for volumetric measurement in the periorbital region. Methods Five hemispherical 3D printed resin models with different sizes and colors, including the flesh-colored models with 6 (Model 1), 4 (Model 2), and 2 (Model 3) mm in diameter, and the black (Model 4) and gray (Model 5) models with 6 mm in diameter, were affixed to the lower eyelid or upper eyelid of 40 volunteers. One researcher used the portable 3D imaging system to collect the 3D images and two raters measured the volume of the models on images. Intra-device, intra-rater and inter-rater reliabilities and accuracy of the volumetric measurement were evaluated by intraclass correlation coefficient (ICC), mean absolute difference (MAD), technical error of measurement (TEM), relative error measurement (REM), and relative TEM (rTEM). Results The intra-device reliability of the 3D imaging system for volumetric measurement in the periorbital region was excellent (ICC = 0.922, MAD = 0.11 mm3, TEM = 0.09 mm3, REM = 0.19%, rTEM = 0.15%). The intra-rater reliability for volumetric measurement of the Model 1 on the lower eyelid was the highest (ICC = 0.953, MAD = 0.08 mm3, TEM = 0.06 mm3, REM = 0.13%, rTEM = 0.11%). The inter-rater reliability for volumetric measurement of the Model 1 on the lower eyelid was the highest (ICC = 0.889, MAD = 0.14 mm3, TEM = 0.11 mm3, REM = 0.24%, rTEM = 0.19%). The accuracy for volumetric measurement of the Model 1 on the lower eyelid was the highest (MAD = 0.24 mm3, REM = 0.43%). Conclusion The portable 3D imaging system proved to measure the volumes of periorbital flesh-colored objects reliably and accurately. This finding demonstrated that this device has great potential for diagnosis, post-operative evaluation, and long-term follow-up of volumetric changes in oculoplastics.
Source: Frontiers in Medicine
Authors: Xuan Zhang, Ji Shao, Ningxin Dai, Huimin Li, Yongwei Guo, Juan Ye, Lixia Lou
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,801
OpenAlex ID: W4413256195
