Title: Developing Interactive Deep Learning to identify ciliary motility and mucociliary clearance defects in chronic inflammatory lung diseases.
Funder: Department of Health and Social Care

Abstract: Chronic inflammatory lung diseases (CILD) lead to progressive irreversible changes in the lungs resulting in chronic respiratory failure and a heavy burden on patients and healthcare systems. Mucociliary clearance is the lungs primary defence mechanism against chronic infection and inflammation. Defects of ciliary motility are either primary, as in PCD, or secondary, as in CF and bronchiectasis, leading to impaired mucous flow across the surface of the airway resulting in retention of infected secretions and the common clinical phenotype of CILD. Early diagnosis is vital to prevent lung damage and to implement appropriate treatment to preserve lung function. High speed video microscopy [HSVM] is used to assess cilia motility and mucous flow. HSVM is time consuming and subjective, requiring skilled, experienced diagnostic scientists. Machine learning offers an opportunity to improve the diagnosis of CILD, minimise subjectivity and significantly reduce the cost and time to analyse samples. This project will use Artificial Intelligence [AI] to develop a deep learning system to identity both healthy and abnormal cilia motility, and mucocilairy clearance, from HSVM data across a range of lung diseases. CILD-AID [CILD-artificial intelligence diagnosis] will be trained by a clinical scientist with years of experience in HSVM analysis. It will be challenged against experienced diagnostic personnel and then validated alongside the clinical diagnostic setup to determine its sensitivity, specificity, accuracy and reliability. Initially, patient HSVM data from the large RBH archive will be used to train the system. After training, validation of CILD-AID will run for 18 months alongside the clinical diagnostic setup before evaluation of the system, writing up findings and a roll out to other diagnostic centres. Results will be published in medical journals, presented at conferences and through our PPI groups. Collaboration with the other UK diagnostic centres aims to distribute this technology nationally.

Lay abstract: To keep our lungs healthy, our airways are lined with multiple hair-like structures called cilia that sweep in a coordinated way to clear mucus, bacteria and debris from the lungs. There are over half a billion people worldwide suffering from chronic inflammatory lung diseases leading to problems with ciliary movement and a build-up of mucus. Some of these conditions are inherited, such as Primary Ciliary Dyskinesia and Cystic Fibrosis, whilst others are secondary to other causes. Chronic inflammation and infection related to mucous retention often results in irreversible lung damage. It is therefore critical to improving patient outcomes to better understand the causes of ciliary function abnormalities and mucous retention in order to improve diagnosis and treatment for all patients. However, diagnosing ciliary dysfunction is complex, with potential for subjective bias, as it relies on looking at airway cells using a microscope and video camera to capture the movement of the cilia and mucus. We aim to use machine learning techniques to develop a tool that can identify healthy and abnormal cilia by measuring changes in mucous flow on the airway surface. This will eliminate subjective outcomes and provide a faster, more accurate and reliable approach to diagnose patients across a wide range of chronic inflammatory respiratory conditions. Artificial Intelligence [AI] has successfully addressed an increasing number of medical imaging challenges. This includes an ongoing project by this research group that uses electron microscope images of cilia to train a machine-learning system to recognise abnormal cilia. We will use a similar approach to examine videos of cilia and mucus movement. This type of AI computing is known as Interactive Deep Learning, a pioneering technique that is expected to significantly accelerate medical research and advance clinical diagnosis. Our project uses the AI platform, COSMONiO NOUS, where clinical experts train self-learning systems in real-time. This use of clinical knowledge and expertise to guide training of AI systems has shown early promise to revolutionise clinical diagnosis and medical research. Royal Brompton Hospital treats over 20,000 patients yearly with chronic inflammatory lung diseases and has a large video archive of mucociliary movement patterns. Better understanding of the impact of chronic inflammation on airway mucociliary clearance will benefit patients: improved diagnostic capabilities to understand the disease mechanisms underlying their chronic lung condition will allow a more targeted approach to improve therapeutic interventions. Dissemination of the project outcomes will be at presented at conferences and through publication as well as through our patient support groups. Our aim is commercialisation of the AI technology to allow integration into clinical health care settings whilst simultaneously advancing the capabilities of researchers to better understand disease mechanisms and study the impact of novel therapies on clinical symptoms.

Research activity codes: 4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES;4.2 EVALUATION OF MARKERS AND TECHNOLOGIES;7.3 MANAGEMENT AND DECISION MAKING
Health categories: CONGENITAL DISORDERS;RESPIRATORY