A5077161257- Chronic Obstructive Pulmonary Disease (COPD) Research
- Lung Cancer Diagnosis and Treatment
- Pleural and Pulmonary Diseases
- COVID-19 diagnosis using AI
- Cystic Fibrosis Research Advances
- Advanced Radiotherapy Techniques
- Radiomics and Machine Learning in Medical Imaging
- Respiratory Support and Mechanisms
- Ultrasound in Clinical Applications
- Pulmonary Hypertension Research and Treatments
- Medical Imaging and Pathology Studies
- Respiratory viral infections research
- Neonatal Respiratory Health Research
- Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
- Artificial Intelligence in Healthcare and Education
- Venous Thromboembolism Diagnosis and Management
- Digital Imaging for Blood Diseases
- Prostate Cancer Treatment and Research
- AI in cancer detection
- Coronary Interventions and Diagnostics
- Cerebral Venous Sinus Thrombosis
- Nerve Injury and Rehabilitation
- Peripheral Nerve Disorders
- Mycobacterium research and diagnosis
- Prostate Cancer Diagnosis and Treatment
University of Liverpool
2008-2025
Aintree University Hospitals NHS Foundation Trust
2025
Liverpool Heart and Chest Hospital
2014-2023
Alder Hey Children's Hospital
2022
Liverpool Heart and Chest Hospital NHS Trust
2020
University of Toronto
2009-2020
Sunnybrook Health Science Centre
2020
Health Sciences Centre
2020
Papworth Hospital
2012
Aintree University Hospital
2008-2009
Coronavirus disease (COVID-19) has caused a worldwide pandemic, putting millions of people's health and lives in jeopardy. Detecting infected patients early on chest computed tomography (CT) is critical combating COVID-19. Harnessing uncertainty-aware consensus-assisted multiple instance learning (UC-MIL), we propose to diagnose COVID-19 using new bilateral adaptive graph-based (BA-GCN) model that can use both 2D 3D discriminative information CT volumes with arbitrary number slices. Given...
Background Although spirometry is an important marker in the management of pulmonary exacerbations cystic fibrosis (CF), it a forced maneuver and can generate aerosol. Therefore, may be difficult to perform some individuals. Dynamic chest radiography (DCR) provides real-time information regarding dynamics alongside fluoroscopic-style thoracic imaging. Purpose To assess effect exacerbation treatment by using both DCR clinical utility participants with CF experiencing exacerbations. Materials...
Neurological complications are well described in SARS-CoV-2, but for the first time we report a case of unilateral diaphragm paralysis occurring early mechanical ventilation respiratory failure due to such an infection. The patient subsequently required tracheostomy and ventilator support 37 days, had increased breathlessness elevated at clinic review 9 months later. Dynamic chest radiography demonstrated persistent with accompanying postural change lung volumes, he underwent surgical...
The CFTR modulator elexacaftor/tezacaftor/ivacaftor (ELX/TEZ/IVA) leads to significant improvement in the symptoms and spirometry of people with cystic fibrosis (pwCF), but little evidence exists understand its effect on respiratory pump function. Dynamic chest radiography (DCR) is a novel cineradiographic tool that identifies tracks wall diaphragm throughout breathing cycle, alongside fluoroscopic images diagnostic quality.In this observational work, we examined DCR 24 pwCF before after...
Objectives Dynamic chest radiography (DCR) is a novel real-time digital fluoroscopic imaging system that produces clear, wide field-of-view diagnostic images of the thorax and diaphragm in motion, alongside metrics on moving structures within thoracic cavity. We describe use DCR measurement motion pilot series cases suspected dysfunction. Methods studied 21 patients referred for assessment function due to suspicious clinical symptoms or (breathlessness, orthopnoea, reduced exercise tolerance...
Dynamic chest radiography (DCR) is a novel, low-dose, real-time digital imaging system where software identifies moving thoracic structures and can automatically calculate lung areas. In an observational, prospective, non-controlled, single-centre pilot study, we compared it with whole-body plethysmography (WBP) in the measurement of volume subdivisions people cystic fibrosis (pwCF).
Introduction Dynamic chest radiography (DCR) uses novel, low-dose radiographic technology to capture images of the thoracic cavity while in motion. Pulmonary function testing is important cystic fibrosis (CF). The tolerability, rapid acquisition and lower radiation cost compared with CT imaging may make DCR a useful adjunct current standards care. Methods analysis This an observational, non-controlled, non-randomised, single-centre, prospective study. study conducted at Liverpool Heart Chest...
•. Smoking-related lung diseases are a heterogeneous group of that can overlap, and coexistence individual disease processes is well recognised. The imaging histopathological findings each these entities, excluding cancer, described. include emphysema, chronic bronchitis, desquamative interstitial pneumonia, respiratory bronchiolitis-related disease, pulmonary Langerhans' cell histiocytosis, acute eosinophilic pneumonia fibrosis. High-resolution CT highly sensitive for the detection...
Abstract The COVID-19 pandemic has been a great challenge to healthcare systems worldwide. It highlighted the need for robust predictive models which can be readily deployed uncover heterogeneities in disease course, aid decision-making and prioritise treatment. We adapted an unsupervised data-driven model—SuStaIn, utilised short-term infectious like COVID-19, based on 11 commonly recorded clinical measures. used 1344 patients from National Chest Imaging Database (NCCID), hospitalised RT-PCR...
Background For lung cancer radiotherapy, respiratory motion broadens dose penumbra, increasing the amount of normal tissues irradiated and reducing target near edge. Traditionally, large PTV margins are used to ensure coverage tumour in presence motion. Unfortunately, organs at risk intersecting with also receive high doses. The objective this work was evaluate a robust strategy account for effects on tissue sparing. Hypothesis Accumulating from 4DCT phases using deformable registration tool...
Abstract Objectives To develop and externally geographically validate a mixed-effects deep learning model to diagnose COVID-19 from computed tomography (CT) imaging following best practice guidelines assess the strengths weaknesses of diagnosis. Design Model development external validation with retrospectively collected data two countries. Setting Hospitals in Moscow, Russia, between March 1, 2020, April 25, 2020. The China Consortium Chest CT Image Investigation (CC-CCII) January 27,...
The automatic analysis of medical images has the potential improve diagnostic accuracy while reducing strain on clinicians. Current methods analyzing 3D-like imaging data, such as computerized tomography imaging, often treat each image slice individual slices. This may not be able to appropriately model relationship between slices.Our proposed method utilizes a mixed-effects within deep learning framework We externally validated this data set taken from different country and compared our...