LDPath partnership with Ibex

UK’s First Rollout of AI-based Cancer Detection for NHS Patients as Pathology Provider LDPath Teams Up with Ibex Medical Analytics

LDPath will deploy Ibex’s Galen™ Prostate, an AI-powered solution to support pathologists in enhancing diagnostic accuracy and efficiency

Tel Aviv, Israel, and London, UK – June 30, 2020 – Ibex Medical Analytics, the pioneer in artificial intelligence (AI)-powered cancer diagnostics and LDPath, a leading provider of digital pathology services to the NHS, today announced the UK’s first rollout of clinical grade AI applications for cancer detection in pathology.

In recent years, a global increase in cancer cases has coincided with a decline in the number of pathologists around the world. Traditional pathology involves manual processes that have remained unchanged for years, where slides are analyzed by pathologists using microscopes, and reporting is often carried out on pieces of paper. Limited availability of pathologists requires couriers to transport glass slides containing tissue samples between different locations to access expert opinions.

The shortage of pathologists in the UK has led to delays in cancer diagnosis, which can take up to six weeks, and together with increased demand, is exerting tremendous pressure on pathology departments while raising concerns about diagnostic accuracy. In addition, such supply and demand issues contribute to critical issues for NHS diagnostics, including breached NICE (National Institute for Health and Care Excellence) cancer guidelines and an increased dependency on expensive temporary solutions.

LDPath, which provides state of the art histopathological imaging and reporting services to 24 NHS trusts throughout the majority of the UK, including large teaching hospitals and district general hospitals, will use its unique position as an innovative and digitally enabled provider within the NHS to integrate Ibex’s Galen™ Prostate solution into its digital pathology workflow. With the CE-marked solution from Ibex, prostate biopsies at LDPath will be reviewed by a highly accurate AI algorithm concomitant with the pathologist’s diagnosis. LDPath pathologists will be alerted in the event of significant discrepancy between their diagnosis and the algorithm’s findings (e.g. a missed cancer), providing a safety net that helps minimize diagnostic errors in the lab by enhancing quality control. During an initial use of Galen Prostate at LDPath, the AI-powered solution identified undetected prostate cancer, as part of an ongoing audit carried out at the request of an NHS Trust.

“We are excited to collaborate with LDPath to bring a paradigm shift for pathology in the UK, and around the world, increasing efficiency and improving accuracy of cancer diagnostics,” said Joseph Mossel, Ibex Medical Analytics CEO and Co-founder. “Cancer cases continue to rise, and with the pathology practice experiencing a worldwide shortage, AI-based technologies can drive new workflows for pathology that will be critical for improving cancer care practices for patients, pathologists, labs and entire healthcare systems.”

“We are proud to be the first UK pathology provider to integrate AI into the digital pathology workflow by partnering with Ibex to improve cancer diagnosis,” said Sanj Lallie, Director of Operations at LDPath. “This is a significant step in realizing the benefits of AI tools within the UK as we continue to redefine traditional workflows across our NHS network. Our NHS clients will benefit from this additional quality assurance measure as well as new service offerings, including singular AI screening of all prostate biopsies within a 24 hour period and UKAS internal audits. The COVID-19 crisis has highlighted the need for advancing innovation and utilising new technologies to improve patient care. By using AI and digital pathology, we are better prepared to continue to work effectively during lockdowns and handle the anticipated surge in the volume of tests and an increase of the pathology workload once we emerge from this pandemic.”

Original text sourced from Ibex