Sajjadi Seyed Mohammad, Mohebbi Alisa, Ehsani Amirhossein, Marashi Amir, Azhdarimoghaddam Aida, Karami Shaghayegh, Karimi Mohammad Amin, Sadeghi Mahsa, Firoozi Kiana, Mohammad Zamani Amir, Rigi Amirhossein, Nayebagha Melika, Asadi Anar Mahsa, Eini Pooya, Salehi Sadaf, Rostami Ghezeljeh Mahsa
Mashhad University of Medical Sciences, Mashhad, Islamic Republic of Iran.
Tehran University of Medical Sciences, Tehran, Islamic Republic of Iran.
Abdom Radiol (NY). 2025 Jan 30. doi: 10.1007/s00261-025-04810-5.
Prior investigations of the natural history of abdominal aortic aneurysms (AAAs) have been constrained by small sample sizes or uneven assessments of aggregated data. Natural language processing (NLP) can significantly enhance the investigation and treatment of patients with AAAs by swiftly and effectively collecting imaging data from health records. This meta-analysis aimed to evaluate the efficacy of NLP techniques in reliably identifying the existence or absence of AAAs and measuring the maximal abdominal aortic diameter in extensive datasets of radiology study reports.
The PubMed, Scopus, Web of Science, Embase, and Science Direct databases were searched until March 2024 to obtain pertinent papers. The RAYYAN intelligent tool for systematic reviews was utilized to screen the studies. The meta-analysis was conducted using STATA v18 software. Egger's test was employed to evaluate publication bias. The Newcastle Ottawa Scale was employed to assess the quality of the listed studies. A plot digitizer was employed to extract digital data.
A total of 39,094 individuals with AAA were included in this analysis. Twenty-seven thousand three hundred twenty-six patients were male, and 11,383 were female. The mean age of the total participants was 73.1 ± 1.25 years. Analysis results for pooled estimation of performance variables such as: The sensitivity, specificity, precision, and accuracy of the implemented NLP model were analyzed as follows: 0.89(0.88-0.91), 0.88 (0.87-0.89), 0.92 (0.89-0.95), and 0.91 (0.89-0.93) respectively. The aneurysm diameter size difference reported in follow-up before and after NLP implementation in the included studies showed a 0.05 cm reduction in size, which was statistically significant.
NLP holds great potential for automating the detection of AAA size and presence in radiology reports, enhancing efficiency and scalability over manual review. However, challenges persist. Variability in report formats, terminology, and unstructured data can compromise accuracy. Additionally, NLP models rely on high-quality, annotated training datasets, which may be incomplete or unrepresentative. While NLP aids in identifying AAA-related data, human oversight is essential to ensure decisions are informed by the patient's broader clinical context. Ongoing algorithm refinement and seamless integration into clinical workflows are key to improving NLP's utility and reliability in this field.
既往对腹主动脉瘤（AAA）自然病史的研究受到样本量小或汇总数据评估不均衡的限制。自然语言处理（NLP）可以通过快速有效地从健康记录中收集影像数据，显著加强对AAA患者的研究与治疗。本荟萃分析旨在评估NLP技术在大量放射学研究报告数据集中可靠识别AAA的存在与否以及测量腹主动脉最大直径的有效性。
检索了PubMed、Scopus、Web of Science、Embase和Science Direct数据库直至2024年3月以获取相关论文。使用RAYYAN智能系统评价工具筛选研究。使用STATA v18软件进行荟萃分析。采用Egger检验评估发表偏倚。采用纽卡斯尔渥太华量表评估纳入研究的质量。使用图形数字化仪提取数字数据。
本分析共纳入39,094例AAA患者。男性患者27,326例，女性患者11,383例。所有参与者的平均年龄为73.1±1.25岁。对性能变量的合并估计分析结果如下：所实施的NLP模型的敏感性、特异性、精确性和准确性分别分析为：0.89(0.88 - 0.91)、0.88(0.87 - 0.89)、0.92(0.89 - 0.95)和0.91(0.89 - 0.93)。纳入研究中报告的在实施NLP前后的随访中动脉瘤直径大小差异显示大小减小了0.05 cm，具有统计学意义。
NLP在自动化检测放射学报告中AAA的大小和存在方面具有巨大潜力，与人工审查相比提高了效率和可扩展性。然而，挑战依然存在。报告格式、术语和非结构化数据的变异性可能会影响准确性。此外，NLP模型依赖高质量的带注释训练数据集，而这些数据集可能不完整或缺乏代表性。虽然NLP有助于识别与AAA相关的数据，但人为监督对于确保决策基于患者更广泛的临床背景至关重要。持续的算法优化以及无缝融入临床工作流程是提高NLP在该领域的实用性和可靠性的关键。
