Systematic review of artificial intelligence and digital-automated tools for screening, diagnosis and characterisation of primary ciliary dyskinesia.
Authors
Affiliations (14)
Affiliations (14)
- Respiratory Physiology Laboratory, Medical School, University of Cyprus, Nicosia, Cyprus.
- Royal Brompton Hospital, Guy's and St Thomas' NHS Foundation Trust, London, UK.
- National Heart and Lung Institute, Imperial College London, London, UK.
- These authors contributed equally to this article.
- Department of Pediatrics, Washington University School of Medicine, Saint Louis, MO, USA.
- Department of Pediatrics, Faculty of Medicine, Al-Quds University, Abu-Dis, Palestine.
- Division of Molecular and Clinical Medicine, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK.
- ISGlobal, Barcelona, Spain.
- Universitat Pompeu Fabra, Barcelona, Spain.
- CIBER Epidemiología y Salud Pública, Barcelona, Spain.
- Northumbria University, Newcastle upon Tyne, UK.
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.
- University of Southampton Faculty of Medicine, School of Clinical and Experimental Sciences, Southampton, UK.
- Primary Ciliary Dyskinesia Centre, NIHR Biomedical Research Centre, University Hospital Southampton NHS Foundation Trust, Southampton, UK.
Abstract
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. 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. 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. 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.