■目前,创伤后应激障碍(PTSD)的诊断主要依靠临床症状和心理量表,寻找有助于诊断的客观指标一直是临床实践和学术研究的挑战。神经成像是发现创伤后应激障碍生物标志物的有用和强大的工具,尤其是功能磁共振成像(fMRI),结构磁共振成像(sMRI)和扩散加权成像(DTI)是最常用的成像技术,可以提供关于大脑功能的多种观点,结构及其连通性。机器学习(ML)是一种新兴的、潜在的强大方法,这引起了人们的兴趣,因为它与神经影像学数据一起用于定义与疾病相关的大脑结构和功能异常,并确定表型,例如帮助医生做出早期诊断。
■根据系统评价和荟萃分析(PRISMA)声明的首选报告项目,进行了系统评价以评估其区分PTSD患者的准确性,TEHC(创伤暴露健康对照),和HC(健康对照)。
■我们搜索了PubMed,Embase,和WebofScience在2023年6月之前使用ML方法和PTSD的常用词,没有语言或时间限制。这篇综述包括13项研究,有了敏感性,特异性,和准确性取自每个出版物或直接从作者那里获得。
■所有ML技术的诊断准确率都在70%以上,支持向量机(SVM)是最常用的技术。这一系列研究揭示了PTSD患者大脑关键区域的神经生物学差异,TEHC,HC。岛屿和杏仁核等区域的连接模式在区分这些群体方面具有特别重要的意义。与PTSD相比,TEHC表现出更多正常的连接模式,为机器学习在创伤后应激障碍诊断中的应用提供有价值的见解。
■与目前可用的任何评估和临床诊断相比,ML技术可以用作早期识别和检测患者以及早期筛查高危人群的有效和非侵入性支持。
UNASSIGNED: At present, the diagnosis of post-traumatic stress disorder(PTSD) mainly relies on clinical symptoms and psychological scales, and finding objective indicators that are helpful for diagnosis has always been a challenge in clinical practice and academic research. Neuroimaging is a useful and powerful tool for discovering the biomarkers of PTSD,especially functional MRI (fMRI), structural MRI (sMRI) and Diffusion Weighted Imaging(DTI)are the most commonly used technologies, which can provide multiple perspectives on brain function, structure and its connectivity. Machine learning (ML) is an emerging and potentially powerful method, which has aroused people\'s interest because it is used together with neuroimaging data to define brain structural and functional abnormalities related to diseases, and identify phenotypes, such as helping physicians make early diagnosis.
UNASSIGNED: According to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) declaration, a systematic
review was conducted to assess its accuracy in distinguishing between PTSD patients, TEHC(Trauma-Exposed Healthy Controls), and HC(healthy controls).
UNASSIGNED: We searched PubMed, Embase, and Web of Science using common words for ML methods and PTSD until June 2023, with no language or time limits. This
review includes 13 studies, with sensitivity, specificity, and accuracy taken from each publication or acquired directly from the authors.
UNASSIGNED: All ML techniques have an diagnostic accuracy rate above 70%,and support vector machine(SVM) are the most commonly used techniques. This series of studies has revealed significant neurobiological differences in key brain regions among individuals with PTSD, TEHC, and HC. The connectivity patterns of regions such as the Insula and Amygdala hold particular significance in distinguishing these groups. TEHC exhibits more normal connectivity patterns compared to PTSD, providing valuable insights for the application of machine learning in PTSD diagnosis.
UNASSIGNED: In contrast to any currently available assessment and clinical diagnosis, ML techniques can be used as an effective and non-invasive support for early identification and detection of patients as well as for early screening of high-risk populations.