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World's First AI-Powered Solution for Gastric Cancer Detection

By LabMedica International staff writers
Posted on 17 Jun 2022
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Image: Galen Gastric assists pathologists in improving gastric cancer detection (Photo courtesy of Ibex Medical)
Image: Galen Gastric assists pathologists in improving gastric cancer detection (Photo courtesy of Ibex Medical)

Gastric cancer is a prominent malignant disease in both men and women worldwide, with over a million new cases every year and a relatively poor prognosis. Pathologists play a crucial role in the detection and diagnosis of gastric cancer, with their assessments being vital for reaching correct treatment decisions by oncologists and improving patient survival rates. Over the last several years there has been an increase in overall cancer incidence, and rapid advances in personalized medicine have resulted in increases in the complexity of cancer diagnosis. Coupled with a global decline in the number of pathologists, these trends have led to growing workloads imposed on pathology departments. Clearly, there is a growing need for automated solutions and decision-support tools that help pathologists detect cancer to the utmost accuracy more rapidly, while enabling comprehensive and affordable quality control. Now, a first-of-its-kind artificial intelligence (AI)-powered solution supports pathologists in the detection of various types of gastric cancer.

Galen Gastric from Ibex Medical Analytics (Tel Aviv, Israel) is the world's first AI-powered solution for gastric cancer detection. Galen Gastric is an integrated diagnostics solution that supports pathologists in the detection of gastric cancer, H. pylori and other important clinical findings and in enabling shorter turnaround times and optimized diagnostic workflows. The solution was developed by a team of pathologists, data scientists and software engineers who implemented advanced deep learning technologies and trained algorithms on more than a million image samples, scanned from biopsy slides digitized using digital pathology.

The Galen Gastric solution has received CE mark follows pioneering results from a blinded, multi-site clinical study in which it demonstrated very high accuracy in detecting various types of gastric cancer, as well as Helicobacter pylori (H. pylori - a common gastric bacterial infection and a precursor for cancer), neuroendocrine lesions, dysplasia, adenoma and additional pathologies. These results validate the robustness of Galen Gastric and support its adoption by pathology institutes that aim to improve diagnostic quality and enhance productivity. Ibex is now partnering with laboratories, hospitals and health systems to implement AI for the diagnosis of gastric biopsies.

"Diagnosis of gastric biopsies remains a challenging task for pathologists. Gastric cancers are relatively scarce, often minute and sometimes easy to miss," said Judith Sandbank, MD, the principal investigator in the study. "We were impressed with the successful outcomes and the very high accuracy demonstrated by Galen Gastric in detection of different cancer types. It's also encouraging to see an AI solution that goes beyond cancer detection to accurately identify H. pylori, and provide additional insights on dysplasia, lymphoma, gastritis and other pathologies."

"We are proud to obtain this first-of-its-kind CE mark for AI-powered GI diagnostics in pathology, following excellent performance in a multi-site clinical study," said Chaim Linhart, PhD, Co-founder and CTO at Ibex Medical Analytics. "We trained the Deep Learning models of Galen Gastric on more than a million image samples from multiple labs to ensure it can accurately detect not only cancer but also a multitude of other clinically relevant features that impact future treatment for patients. Ibex now offers an unprecedented breadth of clinical applications, and this approval will enable our growing customer base to expand the usage of AI technology and provide pathologists with new insights as they review gastric biopsies, improving the quality of cancer diagnosis and care."

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