Alimetry Ltd, Auckland, New Zealand.
Department of Surgery, The University of Auckland, Auckland, New Zealand.
Neurogastroenterol Motil. 2022 Nov;34(11):e14421. doi: 10.1111/nmo.14421. Epub 2022 Jun 14.
Body surface gastric mapping (BSGM) is a new clinical tool for gastric motility diagnostics, providing high-resolution data on gastric myoelectrical activity. Artifact contamination was a key challenge to reliable test interpretation in traditional electrogastrography. This study aimed to introduce and validate an automated artifact detection and rejection system for clinical BSGM applications.
Ten patients with chronic gastric symptoms generated a variety of artifacts according to a standardized protocol (176 recordings) using a commercial BSGM system (Alimetry, New Zealand). An automated artifact detection and rejection algorithm was developed, and its performance was compared with a reference standard comprising consensus labeling by 3 analysis experts, followed by comparison with 6 clinicians (3 untrained and 3 trained in artifact detection). Inter-rater reliability was calculated using Fleiss' kappa.
Inter-rater reliability was 0.84 (95% CI:0.77-0.90) among experts, 0.76 (95% CI:0.68-0.83) among untrained clinicians, and 0.71 (95% CI:0.62-0.79) among trained clinicians. The sensitivity and specificity of the algorithm against experts was 96% (95% CI:91%-100%) and 95% (95% CI:90%-99%), respectively, vs 77% (95% CI:68%-85%) and 99% (95% CI:96%-100%) against untrained clinicians, and 97% (95% CI:92%-100%) and 88% (95% CI:82%-94%) against trained clinicians.
CONCLUSIONS & INFERENCES: An automated artifact detection and rejection algorithm was developed showing >95% sensitivity and specificity vs expert markers. This algorithm overcomes an important challenge in the clinical translation of BSGM and is now being routinely implemented in patient test interpretations.
体表胃映射（BSGM）是一种新的胃动力诊断临床工具，可提供胃肌电活动的高分辨率数据。伪迹污染是传统胃电图可靠测试解释的关键挑战。本研究旨在介绍和验证一种用于临床 BSGM 应用的自动伪迹检测和拒绝系统。
10 名有慢性胃部症状的患者根据标准化方案（使用商业 BSGM 系统（Alimetry，新西兰）产生了各种伪迹）生成 176 次记录。开发了一种自动伪迹检测和拒绝算法，并将其性能与由 3 位分析专家共识标记组成的参考标准进行了比较，然后与 6 位临床医生（3 位未经训练和 3 位接受过伪迹检测训练）进行了比较。采用 Fleiss' kappa 计算组间可靠性。
专家间的组间可靠性为 0.84（95%CI：0.77-0.90），未经训练的临床医生为 0.76（95%CI：0.68-0.83），经过训练的临床医生为 0.71（95%CI：0.62-0.79）。该算法与专家相比，灵敏度和特异性分别为 96%（95%CI：91%-100%）和 95%（95%CI：90%-99%），而与未经训练的临床医生相比，灵敏度和特异性分别为 77%（95%CI：68%-85%）和 99%（95%CI：96%-100%），与经过训练的临床医生相比，灵敏度和特异性分别为 97%（95%CI：92%-100%）和 88%（95%CI：82%-94%）。
开发了一种自动伪迹检测和拒绝算法，其对专家标记的敏感性和特异性均>95%。该算法克服了 BSGM 临床转化中的一个重要挑战，现已在患者测试解释中常规实施。
