Systematic review of artificial intelligence and digital-automated tools for screening, diagnosis and characterisation of primary ciliary dyskinesia.

IF 4.4 3区 医学 Q1 RESPIRATORY SYSTEM
ERJ Open Research Pub Date : 2026-09-28 eCollection Date: 2026-09-01 DOI:10.1183/23120541.01694-2025
Panayiotis Kouis, Mathieu Bottier, Nisreen Rumman, Mia Shanks, Joren Buekers, Dimitrios Megaritis, Andreas Protopapas, Amelia Shoemark, Myrofora Goutaki, Claire Jackson
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引用次数: 0

Abstract

Background: Primary ciliary dyskinesia (PCD) is a rare, multigenic disorder of impaired mucociliary clearance leading to a spectrum of disease including chronic respiratory infection and bronchiectasis. PCD is phenotypically variable causing diagnostic challenges. PCD is underdiagnosed, and when it is made, diagnosis is often delayed. We describe evidence for use of digital-automated applications and/or artificial intelligence (AI) to improve PCD screening, diagnosis and characterisation.

Methods: A systematic literature search (2004-2025) was conducted across PubMed, Embase and BioRxiv/MedRxiv (PROSPERO:CRD42024605689) by members of the ERS BEAT-PCD Clinical Research Collaboration (CRC). Screening and data extraction was performed by two independent reviewers and findings summarised qualitatively. In collaboration with the digital health ERS CONNECT CRC network, a narrative discussion focused on steps to real-world implementation.

Results: Of 770 screened abstracts, 73 full-texts were assessed and 28 PCD-relevant studies with automated digital and/or AI-driven tools were included in the qualitative synthesis. Tools for enhanced ciliary function, ciliary ultrastructure and ciliary protein immunofluorescence analysis were presented in 17 studies. 11 papers presented tools to screen e-health records for PCD, stratify patients by genotype and phenotype, or characterise computed tomography parameters.

Conclusions: Tools are available that could improve PCD diagnostic accuracy and reduce time-to-diagnosis. Studies were often single-centre, retrospective and of small sample size. The development of automated-digital tools or AI-driven tools requires representative patient datasets, human expert judgement to interpret and continual safety auditing. External validation is paramount before adaptation of healthcare systems, with adherence to EU AI Act framework to ensure accountability and safeguard against misuse. Simplified tools and philanthropic partnerships could facilitate implementation of new systems in resource limited settings.

用于原发性纤毛运动障碍筛查、诊断和表征的人工智能和数字自动化工具的系统综述。
背景:原发性纤毛运动障碍(PCD)是一种罕见的、多基因的纤毛粘膜清除受损的疾病,可导致包括慢性呼吸道感染和支气管扩张在内的一系列疾病。PCD是表型可变的,导致诊断困难。PCD未被充分诊断,即使确诊,诊断也常常被延误。我们描述了使用数字自动化应用程序和/或人工智能(AI)来改善PCD筛查、诊断和表征的证据。方法:由ERS BEAT-PCD临床研究合作组织(CRC)成员对PubMed、Embase和BioRxiv/MedRxiv (PROSPERO:CRD42024605689)进行系统文献检索(2004-2025)。筛选和数据提取由两名独立的审稿人进行,并对研究结果进行定性总结。与数字卫生ERS CONNECT CRC网络合作,进行了一场叙述性讨论,重点关注现实世界实施的步骤。结果:770篇筛选摘要中,73篇全文被评估,28篇使用自动化数字和/或人工智能驱动工具的与pcd相关的研究被纳入定性综合。在17项研究中,提出了增强纤毛功能、纤毛超微结构和纤毛蛋白免疫荧光分析工具。11篇论文介绍了筛选PCD电子健康记录、按基因型和表型对患者进行分层或描述计算机断层扫描参数的工具。结论:现有工具可提高PCD的诊断准确性,缩短诊断时间。研究通常是单中心、回顾性和小样本量。自动化数字工具或人工智能驱动工具的开发需要有代表性的患者数据集、人类专家的判断来解释和持续的安全审计。在适应医疗系统之前,外部验证是至关重要的,要遵守欧盟人工智能法案框架,以确保问责制和防止滥用。简化的工具和慈善伙伴关系可以促进在资源有限的情况下实施新系统。
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来源期刊
ERJ Open Research
ERJ Open Research Medicine-Pulmonary and Respiratory Medicine
CiteScore
6.20
自引率
4.30%
发文量
273
审稿时长
8 weeks
期刊介绍: ERJ Open Research is a fully open access original research journal, published online by the European Respiratory Society. The journal aims to publish high-quality work in all fields of respiratory science and medicine, covering basic science, clinical translational science and clinical medicine. The journal was created to help fulfil the ERS objective to disseminate scientific and educational material to its members and to the medical community, but also to provide researchers with an affordable open access specialty journal in which to publish their work.
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