Title: Improving Primary Ciliary Dyskinesia diagnosis using artificial intelligence.
Funder: Department of Health and Social Care

Abstract: Can interactive deep-learning approaches be used to classify normal and abnormal cilia from a limited set of electron microscopy images to improve the diagnosis of primary ciliary dyskinesia? Primary ciliary dyskinesia (PCD) is an inherited autosomal-recessive disorder of motile cilia that results in chronic lung disease, rhinosinusitis, hearing impairment and subfertility. The airway is lined with motile cilia that beat in a coordinated manner to clear mucus, bacteria and debris from the airway. In PCD, defects in ciliary motility result in failure of mucus clearance leading to recurring infections and lung damage. The estimated prevalence of PCD is &sim;1 per 10,000 births, but it is more prevalent in populations where consanguinity is common. An early diagnosis of PCD is vital to prevent lung damage and to implement appropriate treatment to preserve lung function. To diagnose PCD involves a combination of tests, in particular electron microscopy (EM) that is essential for determining the type of ciliary ultrastructural defect and to confirm the diagnosis. EM involves meticulous inspection of >300 well orientated cilia in cross-section, which is time consuming and requires highly skilled and experienced diagnostic scientists. Machine learning offers an opportunity to improve diagnostic accuracy in the specialist centre, but also in a wider setting, reduce time to analyse samples, minimise the subjective element of the current process and significantly reduce costs related to the current number of working hours required per test. The aim of this project is to use interactive deep learning systems to identify both healthy and abnormal cilia from EM image data to provide a faster, less subjective and cost effective approach to assess ciliary ultrastructure. The deep-learning COSMONiO NOUS system will be trained in-house by highly specialised clinical scientists with years of experience in EM analysis of ciliary defects, to recognise normal and abnormal cilia using datasets of EM images generated from historic patient samples. The system will be tested against experienced diagnostic personnel using blinded image datasets before trialling it alongside the current EM diagnostic setup. The accuracy of the system will be calculated by comparing sensitivity and specificity to an experienced diagnostic scientist to determine its long term accuracy and reliability. In month 1-6 images from the archive will be used to teach the system and perform initial testing. Over a period of 16 months the system will be used alongside the current EM diagnostic setup, as part of the validation process, and during this time it will be evaluated against the results reached by the experienced diagnostic personnel. The last 6 months will involve final analysis and evaluation of the system as well as writing up findings and setting up the system at other diagnostic centres. The results will be published in medical journals and at conferences, including the European network (BEAT-PCD). Collaboration with the other UK PCD centres will be established to disseminate this technology. We have the support of the PCD Family Support group who will also disseminate our research outcomes via their website and newsletter and at their annual conference.

Lay abstract: In the airway, cells are lined with many hair-like cilia that sweep in a coordinated way to clear mucus, bacteria and debris from the airway. In primary ciliary dyskinesia (PCD), problems with the movement of the cilia result in mucus build-up that can lead to recurring infections and damage to the lungs. Around 1 in 10,000 people have PCD in the UK, but it s much more common in some communities. Early diagnosis is important to help prevent lung damage in childhood and to keep the lungs working as well as they can. Diagnosis is complex and currently relies on looking at cilia (taken from the nose) and checking them with a powerful electron microscope. It can be lengthy and difficult to make a diagnosis and experts can have different opinions on what the microscopy shows. The aim of this project is to use Artificial Intelligence computing techniques to identify both healthy and abnormal cilia from electron microscopy images in order to provide a faster, accurate and reliable approach to evaluate ciliary structure. In recent years, Artificial Intelligence has successfully addressed an increasing number of medical-imaging challenges. Powerful self-learning systems are trained to recognise a range of medical conditions. One specific method, Deep Learning, is a pioneering technique that s expected to significantly accelerate medical research. One of the downsides of existing Deep Learning systems is that there is minimal involvement of medical experts and instead usually relies on data scientists without a medical background to interpret. This leads to delays and inaccuracies when setting up the Artificial Intelligence system. This project will be amongst the first worldwide to use a new interactive Artificial Intelligence technology, known as COSMONiO NOUS, that allows clinical experts to train self-learning systems by themselves in real-time. In doing so, they will use their knowledge, intuition and expertise to guide the learning process of the Artificial Intelligence system without the direct involvement of a data scientist. If successful, such a technology could revolutionise diagnosis and help with medical research. Royal Brompton Hospital treats over 300 patients with PCD each year. They will use their archive of over 1000 patient samples, to train the Artificial Intelligence system and more easily identify PCD in patients. The results will be published in medical journals and conferences, including the European network (BEAT-PCD). Collaboration with the other UK PCD centres will be established to disseminate this technology. This will help lead to further developments in the COSMONiO NOUS Artificial Intelligence technology with the aim to reduce the global number of unconfirmed PCD cases (approximately 44% of all patients treated for PCD), as well as provide the basis for computer assisted diagnosis in a range of medical conditions.

Research activity codes: 4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES;4.2 EVALUATION OF MARKERS AND TECHNOLOGIES
Health categories: RESPIRATORY