PDF(Pubmed)
Abstract：
Aspiration caused by dysphagia is a prevalent problem that causes serious health consequences and even death. Traditional diagnostic instruments could induce pain, discomfort, nausea, and radiation exposure. The emergence of wearable technology with computer-aided screening might facilitate continuous or frequent assessments to prompt early and effective management. The objectives of this review are to summarize these systems to identify aspiration risks in dysphagic individuals and inquire about their accuracy. Two authors independently searched electronic databases, including CINAHL, Embase, IEEE Xplore® Digital Library, PubMed, Scopus, and Web of Science (PROSPERO reference number: CRD42023408960). The risk of bias and applicability were assessed using QUADAS-2. Nine (n = 9) articles applied accelerometers and/or acoustic devices to identify aspiration risks in patients with neurodegenerative problems (e.g., dementia, Alzheimer\'s disease), neurogenic problems (e.g., stroke, brain injury), in addition to some children with congenital abnormalities, using videofluoroscopic swallowing study (VFSS) or fiberoptic endoscopic evaluation of swallowing (FEES) as the reference standard. All studies employed a traditional machine learning approach with a feature extraction process. Support vector machine (SVM) was the most famous machine learning model used. A meta-analysis was conducted to evaluate the classification accuracy and identify risky swallows. Nevertheless, we decided not to conclude the meta-analysis findings (pooled diagnostic odds ratio: 21.5, 95% CI, 2.7-173.6) because studies had unique methodological characteristics and major differences in the set of parameters/thresholds, in addition to the substantial heterogeneity and variations, with sensitivity levels ranging from 21.7% to 90.0% between studies. Small sample sizes could be a critical problem in existing studies (median = 34.5, range 18-449), especially for machine learning models. Only two out of the nine studies had an optimized model with sensitivity over 90%. There is a need to enlarge the sample size for better generalizability and optimize signal processing, segmentation, feature extraction, classifiers, and their combinations to improve the assessment performance. Systematic Review Registration: (https://www.crd.york.ac.uk/prospero/), identifier (CRD42023408960).
摘要：
由吞咽困难引起的误吸是导致严重健康后果甚至死亡的普遍问题。传统的诊断仪器会引起疼痛，不适,恶心,和辐射暴露。具有计算机辅助筛查的可穿戴技术的出现可能有助于连续或频繁的评估，以促进早期和有效的管理。本综述的目的是总结这些系统，以识别吞咽困难个体的误吸风险并询问其准确性。两位作者独立搜索了电子数据库，包括CINAHL，Embase,IEEEXplore®数字图书馆，PubMed,Scopus,和WebofScience（PROSPERO参考号：CRD42023408960）。使用QUADAS-2评估偏倚和适用性的风险。九篇（n=9）文章应用了加速度计和/或声学设备来识别患有神经退行性问题的患者的抽吸风险（例如，痴呆症,阿尔茨海默病），神经源性问题(例如，中风,脑损伤)，除了一些先天性异常的儿童，使用视频透视吞咽研究（VFSS）或光纤内窥镜吞咽评估（FEES）作为参考标准。所有研究都采用了具有特征提取过程的传统机器学习方法。支持向量机(SVM)是最著名的机器学习模型。进行荟萃分析以评估分类准确性并识别有风险的燕子。然而，我们决定不总结荟萃分析结果（合并诊断优势比：21.5，95％CI，2.7-173.6），因为研究具有独特的方法学特征和参数/阈值集的主要差异，除了实质性的异质性和变化之外，研究之间的敏感性水平从21.7%到90.0%不等。小样本量可能是现有研究中的一个关键问题(中位数=34.5，范围18-449)，尤其是机器学习模型。九项研究中只有两项具有灵敏度超过90％的优化模型。有必要扩大样本量以获得更好的泛化性并优化信号处理，分割，特征提取,分类器,以及它们的组合来提高评估绩效。系统审查注册：（https：//www。crd.约克。AC.英国/繁荣/)，标识符(CRD42023408960)。
