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使用智能手机摄像头估计上颌窦体积。

Estimating Maxillary Sinus Volume Using Smartphone Camera.

作者信息

Meliadis Christoforos, Feng Emily, Johnson Ezekiel, Zhu Wendy, Gopi Paramesh, Mohan Vivek, Hwang Peter H, Johnson Jacob, Lin Bryant Y

机构信息

University of California San Francisco CA 94143 USA.

San Francisco Otolaryngology Medical Group San Francisco CA 94108 USA.

出版信息

IEEE Open J Eng Med Biol. 2024 Dec 12;6:256-260. doi: 10.1109/OJEMB.2024.3516320. eCollection 2025.

Abstract

This study aims to introduce a novel method for estimating maxillary sinus volume using smartphone technology, providing an accessible alternative to traditional imaging techniques. We recruited 40 participants to conduct a comparative analysis between Computed Tomography (CT) and face scans obtained using an Apple iPhone. Utilizing Apple's ARKit for 3D facial mesh modeling, we estimated sinus dimensions based on established craniofacial landmarks and calculated the volume through a geometric approximation of the maxillary sinus. We demonstrated a high degree of agreement between CT and face scans, with Mean Absolute Percentage Errors (MAPE) of 8.006 ± 8.839% (Width), 6.725 ± 4.595% (Height), 9.952 ± 6.733% (Depth), and 10.429 ± 7.409% (Volume). These results suggest the feasibility of this non-invasive approach for clinical use. This method aligns with the growing focus on telemedicine, presenting significant reductions in healthcare costs and radiation exposure from CT scans. It marks a substantial advancement in otolaryngology and maxillofacial surgery, showcasing the integration of smartphone technology in medical diagnostics and opening avenues for innovative, patient-friendly, and cost-effective healthcare solutions.

摘要

本研究旨在介绍一种利用智能手机技术估算上颌窦容积的新方法,为传统成像技术提供一种便捷的替代方案。我们招募了40名参与者,对计算机断层扫描(CT)和使用苹果iPhone获得的面部扫描进行比较分析。利用苹果的ARKit进行三维面部网格建模,我们根据既定的颅面标志点估算鼻窦尺寸,并通过对上颌窦的几何近似计算其容积。我们证明了CT和面部扫描之间具有高度一致性,平均绝对百分比误差(MAPE)为:宽度8.006±8.839%、高度6.725±4.595%、深度9.952±6.733%、容积10.429±7.409%。这些结果表明这种非侵入性方法用于临床的可行性。该方法与对远程医疗日益增长的关注相一致,显著降低了医疗成本和CT扫描的辐射暴露。它标志着耳鼻咽喉科和颌面外科的重大进展,展示了智能手机技术在医学诊断中的整合,并为创新、患者友好且具有成本效益的医疗解决方案开辟了道路。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d40f/11793482/cc8cccfd6a53/melia1-3516320.jpg

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