A5036248108- COVID-19 diagnosis using AI
- Ultrasound in Clinical Applications
- Radiomics and Machine Learning in Medical Imaging
- Lung Cancer Diagnosis and Treatment
- Radiology practices and education
- COVID-19 Clinical Research Studies
- Artificial Intelligence in Healthcare and Education
- SARS-CoV-2 detection and testing
Oxford University Hospitals NHS Trust
2022-2024
Government of the United Kingdom
2023
University of Oxford
2023
Uncertainty in patients' COVID-19 status contributes to treatment delays, nosocomial transmission, and operational pressures hospitals. However, the typical turnaround time for laboratory PCR remains 12-24 h lateral flow devices (LFDs) have limited sensitivity. Previously, we shown that artificial intelligence-driven triage (CURIAL-1.0) can provide rapid screening using clinical data routinely available within 1 of arrival hospital. Here, aimed improve from emergency department availability...
Artificial intelligence (AI)-assisted image interpretation is a fast-developing area of clinical innovation. Most research to date has focused on the performance AI-assisted algorithms in comparison with that radiologists rather than evaluating algorithms' impact clinicians who often undertake initial routine practice. This study assessed diagnostic frontline acute care for detection pneumothoraces (PTX).
Background: Artificial Intelligence (AI)-assisted image interpretation is a fast-developing area of clinical innovation. To date research has focussed on AI’s performance with radiologists rather than, non-radiologist clinicians who often undertake initial interpretation. This study assessed the impact AI-assisted diagnostic frontline acute care for detection pneumothoraces.Methods: A multicentre blinded fully-crossed multi-case multi-reader was conducted between October 2021 to January...
Abstract Background During and after the COVID pandemic, online learning became a key component in most undergraduate post-graduate training. The non-specific symptoms of COVID-19 limitations available diagnostic tests can make it difficult to detect diagnose acute care settings. Accurate identification SARS-CoV-2 related changes on chest x-ray (CXR) by frontline clinicians involved direct patient Emergency Department (ED) is an important skill. We set out measure accuracy ED detecting CXRs...
Aims/Objectives/Background The non-specific symptoms of COVID-19 and the lack a highly-sensitive point-of-care test make it difficult to reliably detect diagnose in acute care settings. early identification using chest X-rays (CXR) Emergency Department (ED) is crucial skill for frontline clinicians. We wanted measure accuracy ED clinicians detecting CXR changes assess improvement an adaptive online learning module. Methods/Design working across five hospitals Thames Valley medicine Research...