El Ghebouli Ayoub, Mombereau Amaël, Haïssaguerre Michel, Dubois Rémi, Bear Laura R
University Bordeaux, Institut national de la sante et de la recherche medicale (INSERM), U-1045, IHU Liryc, Le Centre de Recherche Cardio-Thoracique de Bordeaux (CRCTB), Bordeaux, France.
CHU de Bordeaux, Cardiology-Electrophysiology and Stimulation Department, Institut national de la sante et de la recherche medicale (INSERM), U-1045, Bordeaux, France.
Front Physiol. 2025 Mar 12;16:1504319. doi: 10.3389/fphys.2025.1504319. eCollection 2025.
Body surface potential maps (BSPMs) derived from multi-channel ECG recordings enable the detection and diagnosis of electrophysiological phenomena beyond the standard 12-lead ECG. In this work, we developed two AI-based methods for the automatic detection of location of the electrodes used for BSPM: a rapid method using a specialized 3D Depth Sensing (DS) camera and a slower method that can use any 2D camera. Both methods were validated on a phantom model and in 7 healthy volunteers. With the phantom model, both 3D DS camera and 2D camera method achieved an average localization error less than 2 mm when compared to CT-scan or an Electromagnetic Tracking System (ETS). With healthy volunteers, the 3D camera yielded average 3D Euclidean distances ranging from 2.61 ± 1.2 mm to 5.78 ± 3.09 mm depending on the patient, similar to that seen with 2D camera (ranging from 2.45 ± 1.32 mm to 5.88 ± 2.73 mm). These results demonstrate high accuracy and provide practical alternatives to traditional imaging techniques, potentially enhancing the interest of BSPMs in a clinical setting.
从多通道心电图记录中得出的体表电位图（BSPM）能够检测和诊断超出标准12导联心电图范围的电生理现象。在这项工作中，我们开发了两种基于人工智能的方法，用于自动检测BSPM所用电极的位置：一种是使用专门的3D深度感应（DS）相机的快速方法，另一种是可以使用任何2D相机的较慢方法。这两种方法均在体模模型和7名健康志愿者身上进行了验证。对于体模模型，与CT扫描或电磁跟踪系统（ETS）相比，3D DS相机和2D相机方法的平均定位误差均小于2毫米。对于健康志愿者，3D相机得出的平均3D欧几里得距离根据患者不同在2.61±1.2毫米至5.78±3.09毫米之间，与2D相机所见相似（范围为2.45±1.32毫米至5.88±2.73毫米）。这些结果证明了高精度，并为传统成像技术提供了实用的替代方法，可能会提高BSPM在临床环境中的应用价值。
