ADOPT: Improving Diagnosis of Pulmonary Hypertension With AI and Echo

Detect patients with pulmonary hypertension (PH) with the novel artificial intelligence tool (AIT),Detect patients without pulmonary hypertension (PH) with the novel artificial intelligence tool (AIT),Detect patients with pre-capillary pulmonary hypertension (PH) with the novel artificial intelligence tool (AIT),Detect patients with post-capillary pulmonary hypertension (PH) with the novel artificial intelligence tool (AIT),Compare the artificial intelligence tool (AIT) performance for detecting pulmonary hypertension (PH) with the current probability criteria,Evaluate early detection capabilities of the artificial intelligence tool (AIT) compared to standard of care clinical diagnosis

Pulmonary Hypertension (PH) is a condition caused by high blood pressure in the blood vessels
 that carry blood to the lungs. It can cause severe breathlessness and failure of the right
 side of the heart. Sadly it is often fatal, and life expectancy ranges from months to years.
 For some subtypes of PH, effective treatments exist which can improve life expectancy and
 quality-of-life. Accurate tools for the assessment of PH are therefore essential so that
 life-saving medications can be started earlier.
 
 In existing diagnostic pathways, evidence for the suspicion of PH is frequently overlooked,
 significantly delaying the time to diagnosis. Echocardiography (echo) is a quick, safe and
 well-tolerated test requested to investigate breathless patients, and which can provide
 useful information about the suspicion of PH. However, outside of specialist PH centres,
 doctors may not routinely look for and comment on the presence of clues to possible PH.
 
 The investigators think that using Artificial Intelligence (AI) techniques to read echo's
 could make their interpretation faster and more reliable. There may also be subtle clues to
 the presence or severity of PH on echo, less recognisable to the human eye, which AI can
 identify.
 
 In this study the investigators will gather echo images from 5 specialist PH hospitals across
 the UK which have all been anonymised (patient's name and personal details removed). These
 will all be historic scans (i.e. have already taken place) and will be grouped into those
 with PH present (including PH sub-type) or absent. These anonymised echo images will be used
 to develop and train an AI tool to identify scans where PH is present, including which
 specific type of PH may be present. The developed AI tool will then be tested on a separate
 group of scans (not used in the training stage) to validate its performance.