AI enabled biomarker identification from exhaled breath condensates for early detection of secondary infection in COPD patients

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Current diagnosis of respiratory pathogens can require invasive sampling which makes routine screening labour intensive, unpleasant and impractical for patients. As bovine TB infections can be diagnosed from biomarkers present in exhaled breath, we wish to further develop this approach for the diagnosis of infections in chronic obstructive pulmonary disease (COPD) patients. COPD is characterised by longterm breathing problems which can be exacerbated by secondary infections. Worldwide nearly 12% of people have COPD which causes 3 million deaths annually. Using and developing advanced imaging and machine learning techniques to analyse mass spectrometry (MS) data, we aim to identify specific exhaled biomarkers that will diagnose secondary infections prior to the onset of symptoms. This would allow proactive treatment to prevent exacerbations and hospitalisation. Detection systems for these exhaled biomarkers could ultimately be developed to enable noninvasive rapid monitoring at the point of care.The proposed work will address three questions:1. Can imaging and machine learning techniques identify diagnostic biomarkers for secondary infections in MS data of exhaled breath samples from COPD patients?2. Can identified biomarkers be used to detect/predict developing infections or exacerbations?3. How well do biomarkers identified by different techniques correlate?Use of publicly available datasets will allow the student to become familiar with the computational methods involved on related data sets and to test initial methods for their ability to segregate known groupings as part of the development of potential solutions while clinical samples are collected. We will sample an existing cohort of COPD patients to which we have access. Breath samples will be collected during infections (infected) and on recovery (normal) to enable identification of infection specific biomarkers. Samples will be analysed by MS to identify the molecules present and their concentrations. The data from the MS covering the whole mass range will be converted to a heat map depicting the relative mass-tocharge (m/z) intensity distribution of the sample. This preprocessed data will then be analysed using sophisticated image processing techniques. The initial MS data will also be cleaned and prepared for a machine learning based approach for cross validation.Stage two will apply state-of-the-art pattern recognition methods such as DeepCluster and machine learning algorithms including support vector machine, random forests, and artificial neural networks to the processed MS data to classify COPD patients as infected or not. The student will then use this as a starting point to develop more advanced algorithms to improve the accuracy of biomarker identification to enable prediction of possible infections. During this refinement phase, the SLS academics will contribute their expertise to ensure identified biomarkers are biologically relevant and meaningful. The data will also be analysed by a machine learning approach to provide in silico validation of the results from the imaging algorithms and provide further confidence to the identification of putative biomarkers of infection.In stage three, predicted biomarkers from stage 2 will be validated using unknown clinical samples. Working with clinical collaborators, blinded samples will be analysed by MS and the data used by the developed algorithms to assign each as normal or infected based on the biomarkers present. Once assigned by the computational approach, samples will be unblinded to assess the success of this approach, and allow further refinement.This work will contribute to the development of effective advanced image processing and machine learning algorithms for use in clinical settings. It may also provide advances in healthcare support systems that allow proactive clinicalmanagement of infections to prevent COPD exacerbations and reduce hospitalisations.