背景:完整的报告对于临床研究至关重要。然而,放射学期刊对报告指南的认可仍不清楚.Further,作为一个广泛利用人工智能(AI)的领域,采用一般和人工智能报告准则对于提高放射学研究的质量和透明度是必要的。这项研究旨在调查一般报告指南的认可以及放射学期刊中AI在医学成像中的应用。并探索相关的期刊特征变量。
方法:这项荟萃研究筛选了放射学杂志,核医学和医学影像类别,2022年期刊引文报告的科学引文索引扩展,并排除不发表原创性研究的期刊,在非英语语言中,和作者的说明不可用。对15条一般报告指南和10条人工智能报告指南的认可使用五级工具进行了评级:“积极强大”,\"活动弱\",“被动温和”,“被动弱”,和“无”。通过逻辑回归分析评估背书与期刊特征变量之间的关联。
结果:我们纳入了117个期刊。认可的前五名报告准则是CONSORT(合并报告试验标准,58.1%,68/117),PRISMA(系统评价和荟萃分析的首选报告项目,54.7%,64/117),STROBE(加强流行病学观察研究的报道,51.3%,60/117),STARD(诊断准确性报告标准,50.4%,59/117),和ARRIVE(体内实验的动物研究报告,35.9%,42/117)。实施最广泛的人工智能报告指南是CLAIM(医学影像人工智能清单,1.7%,2/117),而其他9个人工智能报告指南没有提到。期刊影响因子四分位数和出版商与放射学期刊报告指南的认可有关。
结论:一般报告指南认可在放射学期刊中并不理想。人工智能在医学成像中应用的报告指南的实施非常低。应加强其采用,以提高放射学研究报告的质量和透明度。
Complete reporting is essential for clinical research. However, the endorsement of reporting
guidelines in radiological journals is still unclear. Further, as a field extensively utilizing artificial intelligence (AI), the adoption of both general and AI reporting guidelines would be necessary for enhancing quality and transparency of radiological research. This study aims to investigate the endorsement of general reporting guidelines and those for AI applications in medical imaging in radiological journals, and explore associated journal characteristic variables.
This meta-research study screened journals from the Radiology, Nuclear Medicine & Medical Imaging category, Science Citation Index Expanded of the 2022 Journal Citation Reports, and excluded journals not publishing original research, in non-English languages, and instructions for authors unavailable. The endorsement of fifteen general reporting
guidelines and ten AI reporting
guidelines was rated using a five-level tool: \"active strong\", \"active weak\", \"passive moderate\", \"passive weak\", and \"none\". The association between endorsement and journal characteristic variables was evaluated by logistic regression analysis.
We included 117 journals. The top-five endorsed reporting guidelines were CONSORT (Consolidated Standards of Reporting Trials, 58.1%, 68/117), PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses, 54.7%, 64/117), STROBE (STrengthening the Reporting of Observational Studies in Epidemiology, 51.3%, 60/117), STARD (Standards for Reporting of Diagnostic Accuracy, 50.4%, 59/117), and ARRIVE (Animal Research Reporting of In Vivo Experiments, 35.9%, 42/117). The most implemented AI reporting
guideline was CLAIM (Checklist for Artificial Intelligence in Medical Imaging, 1.7%, 2/117), while other nine AI reporting
guidelines were not mentioned. The Journal Impact Factor quartile and publisher were associated with endorsement of reporting guidelines in radiological journals.
The general reporting
guideline endorsement was suboptimal in radiological journals. The implementation of reporting guidelines for AI applications in medical imaging was extremely low. Their adoption should be strengthened to facilitate quality and transparency of radiological study reporting.