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Computing abnormalities in chest-xray

48 months
Approved budget:
Associate Professor Alys Clark
Health issue:
Proposal type:
Emerging Researcher First Grant
Lay summary
Bronchiectasis is a chronic lung disease with high prevalence in New Zealand compared with other developed nations. It is important to diagnose bronchiectasis early to prevent unneccessary disease progression. Bronchiectasis often eventuates after multilple lung infections for which patients have undergone chest xrays (CXRs), which are 2D and low resolution. CXRs are easy to obtain and require low radiation doses. However, high resolution computed tomography (HRCT), which provides 3D reconstructions of lung tissue is required for diagnosis. We propose a reconcilliation of HRCT and CXR to identify the digital features of bronchiectasis in CXR. HRCT images from patients with confirmed bronchiectasis will be projected into 2D space and digital features of CXR corresponding to known abnormalities identified. Development of digital image analysis to identify abnormalities in CXR will benefit health by improving the chances of early detection of diseases like bronchiectasis, whilst minimising the use of high radiation imaging.