Kahl Fabian, Kapsecker Maximilian, Nissen Leon, Bresser Laura, Heinemann Marie, Reimer Lara Marie, Jonas Stephan M
Institute for Digital Medicine, University Hospital Bonn, Bonn, Germany.
TUM School of Computation, Information and Technology, Technical University of Munich, Munich, Germany.
Hamostaseologie. 2024 Dec;44(6):446-458. doi: 10.1055/a-2415-8646. Epub 2024 Dec 10.
This systematic review aims to comprehensively survey digital technologies used in the prevention, diagnosis, and treatment of hereditary blood coagulation disorders.
The systematic review was performed according to the PRISMA guidelines. A systematic search was conducted on PubMed on January 29, 2024. Articles were excluded if they were reviews, meta-analyses, or systematic reviews. Articles were included if they were published from January 1, 2014, onward, written in English, described an actual application of digital tools, were in the context of hereditary coagulation disorders, and involved studies or trials on humans or human data with at least three subjects.
The initial PubMed search on January 29, 2024, identified 2,843 articles, with 672 from January 1, 2014, onward. After screening, 21 articles met the exclusion and inclusion criteria. Among these, 12 focused on artificial intelligence (AI) technologies and 9 on digital applications. AI was predominantly used for diagnosis (five studies) and treatment (four studies), while digital applications were mainly used for treatment (eight studies). Most studies addressed hemophilia A, with a smaller number including hemophilia B or von Willebrand disease.
The findings reveal a lack of intervention studies in the prevention, diagnosis, and treatment. However, digital tools, including AI and digital applications, are increasingly used in managing hereditary coagulation disorders. AI enhances diagnostic accuracy and personalizes treatment, while digital applications improve patient care and engagement. Despite these advancements, study biases and design limitations indicate the need for further research to fully harness the potential of these technologies.
本系统评价旨在全面调查用于遗传性血液凝固障碍预防、诊断和治疗的数字技术。
本系统评价按照PRISMA指南进行。于2024年1月29日在PubMed上进行了系统检索。如果文章是综述、荟萃分析或系统评价，则予以排除。如果文章发表于2014年1月1日之后，用英文撰写，描述了数字工具的实际应用，处于遗传性凝血障碍的背景下，并且涉及对人类或人类数据进行的至少有三名受试者的研究或试验，则予以纳入。
2024年1月29日在PubMed上的初始检索识别出2843篇文章，其中672篇发表于2014年1月1日之后。经过筛选，21篇文章符合排除和纳入标准。其中，12篇关注人工智能（AI）技术，9篇关注数字应用。AI主要用于诊断（五项研究）和治疗（四项研究），而数字应用主要用于治疗（八项研究）。大多数研究涉及甲型血友病，少数研究包括乙型血友病或血管性血友病。
研究结果显示在预防、诊断和治疗方面缺乏干预性研究。然而，包括AI和数字应用在内的数字工具在遗传性凝血障碍的管理中使用得越来越多。AI提高了诊断准确性并使治疗个性化，而数字应用改善了患者护理和参与度。尽管取得了这些进展，但研究偏差和设计局限性表明需要进一步研究以充分发挥这些技术的潜力。
