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DIAGNOSTICS


these digital images aids pathologists in the accurate and rapid recognition of disease patterns, which may be particularly useful in instances where there is a small area of cancer in a large sample. This means that pathologists can use AI to diagnose cancer faster and at lower cost, which has obvious advantages for the NHS and patients. In addition, the development of image analysis algorithms using machine learning (a branch of AI) can aid molecular profiling for personalised cancer treatment. To this end, we have been working with the NHS and academia on the very successful Northern Pathology Imaging Cooperative (NPIC) in Leeds. It began with a £10.1m investment from UK Research and Innovation to expand a digital pathology and AI programme across the North of England. The consortium – which is led by the University of Leeds and Leeds Teaching Hospitals – embraces a network of nine NHS hospitals, seven universities, Roche Diagnostics and a further ten industry- leading medical technology companies. It is now set to become a globally-leading centre for applying AI research to cancer diagnosis. This collaboration aims to enhance our understanding of the unique characteristics of different cancers to enable the delivery of precision medicines and, ultimately, achieve the best possible outcomes for patients. Using AI algorithms to speed up and optimise the diagnosis and treatment of cancer has the potential to revolutionise how cancer care is delivered and this will only become more vital as we recover from COVID-19. We also partner with academia on a number of other projects exploring the utility of AI in enabling earlier diagnoses. This includes The Integration and Analysis of Data


using Artificial Intelligence to Improve Patient Outcomes with Thoracic Diseases (DART) project, which is focused on combining clinical, imaging and molecular data using AI algorithms to more accurately and quickly diagnose and characterise lung cancer with fewer invasive clinical procedures. Professor Fergus Gleeson, consultant radiologist and chief investigator for the DART programme at The University of Oxford, says: “The novel linking of diagnostic technologies, patient outcomes and biomarkers using AI has the potential to make a real difference to how people with suspected lung cancer are investigated. By differentiating between cancers and non-cancers more accurately based on the initial CT scan and blood tests, we hope to remove the delay and possible harm caused by repeat scans and further invasive tests. If successful, this has the potential to reduce patient anxiety, diagnose cancers earlier and lead to more rapid treatment, improving survival rates and time and cost savings.”


Tackling cardiovascular disease AI innovation is making headway across other disease areas too. Research funded by the British Heart Foundation (BHF) has led to AI technology being used across the NHS which can identify patients most likely to have a heart attack up to nine years before danger occurs.


Along with cancer, cardiovascular disease is a major area for concern. Patients with illnesses like heart failure are at an increased risk of health complications due to COVID-19 infection and experience worse outcomes as a result. We are also seeing a rise in people with heart failure due to COVID-19.5 We produced a report last year, in collaboration with leading heart failure charity the Pumping Marvellous Foundation, which included research into the impact of COVID-19 on heart failure. From a survey of 625 heart failure patients in June to July 2020, we discovered that nearly three in 10 (29.3%) said their symptoms had got worse during the pandemic. Nearly four in 10 patients (38.4%) had a hospital or GP appointment for heart failure cancelled due to COVID-19, while over a third (33.4%) admitted they were avoiding going to the doctors to discuss the condition.6 More recently, the BHF has highlighted the need for urgent action after new research estimated that 23,000 heart failure diagnoses were missed in England during the pandemic.6


It is estimated that eventually 350,000 people could benefit from the checks every year.4


the number of echocardiograms (or ‘echos’) fell by around two thirds (67%) across April and May last year compared to February before the first lockdown.7 Waiting lists for echos are building up: by the end of May 2020, around 62% of people referred for an echo had been on the waiting list for six weeks or more, compared to just 4% at the end of February.7


This


is where integration can help – and so can diagnostics. Natriuretic peptide testing (NT-


AUGUST 2021 WWW.CLINICALSERVICESJOURNAL.COM l 71


Furthermore, they found that


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